CerebraGloss: Instruction-Tuning a Large Vision-Language Model for Fine-Grained Clinical EEG Interpretation
Authors: Wei Gu
Luo Tianming
Qiran Zhang
Mohan Ye
Xiao Shen
Wenxin Chen
Yunhuan Li
Yichen Zhang
Jing Hong
Bao-liang Lu
Wei-Long Zheng
Venue: ICLR 2026 Poster (2026)
Keywords: large vision-language model
instruction-tuning
EEG
clinical
Primary area: foundation or frontier models, including LLMs
TL;DR: We present CerebraGloss, the first instruction-tuned LVLM for fine-grained clinical EEG analysis, enabled by a novel automated data generation pipeline and evaluated on our new comprehensive benchmark, CerebraGloss-Bench.

Abstract: Interpreting clinical electroencephalography (EEG) is a laborious, subjective process, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the scarcity of datasets pairing EEG visualizations with fine-grained, expert-level annotations. We address this by introducing CerebraGloss, an instruction-tuned LVLM for nuanced EEG interpretation. We first introduce a novel, automated data generation pipeline, featuring a bespoke YOLO-based waveform detector, to programmatically create a large-scale corpus of EEG-text instruction data. Using this data, we develop CerebraGloss, the first model of its kind capable of unified, generative analysis—performing tasks from detailed waveform description to multi-turn, context-aware dialogue. To evaluate this new capability, we construct and release CerebraGloss-Bench, a comprehensive benchmark for open-ended EEG interpretation. CerebraGloss demonstrates strong performance, surpassing leading LVLMs, including proprietary models like GPT-5, on this benchmark and achieving a new state-of-the-art on the TUSZ seizure detection task. Models, benchmark and tools are available at https://github.com/iewug/CerebraGloss.

Published as a conference paper at ICLR 2026
CEREBRAGLOSS: INSTRUCTION-TUNNNN A LARGE VISION-LANGUAGE MODEL FOR FINE-GRAINEL CLINICAL EEG INTERPRETATION
Wei Gu', Tianming Luo', Qiran Zhang', Mohan Ye', Xiao Shen', Wenxin Chen', Yunhuan Li', Yichen Zhang", Jing Hong', Bao-Liang Lu', Wei-Long Zheng!* 'Shanghai Jiao Tong University. TTsinguua University. RRiJJn Hospital, Shanghai Jiao Tong University School of Medicine
ABSTRACT
Interpreting clinical electreencephalooraapyy (EEG) is laborious, subjective pro- cess, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the scarcity of datasets pair- ing EEG visualizations with fine- -ne-graindd expert-level annotations. We address this by introducing CerebraGloss, an instruction-tuned LVLM for nuanced EEG interpretation. We first introduce a novel, automated data generation pipeline, featuring bespoke YOLO-based waveform detector, to programmatically cre- ate a large-scale corpus of EEG-text instruction data. Using this data, we de- velop CerebraGloss, the first model of its kind capable of unified, generative feliyyis--erffomming tasks from detailed waveform description to multi-turn, context-aware dialogue. To evaluate this capability, we construct and re- lease cerebraGloss-Bench. a comprehensive benchmark for open-ended EEG in- terpretation. CerebraGloss demonstrates strong performance, surpassing leading LVLMs, including proprietary models like GPT-5, on this benchmark and achiev- ing new state-of-the- of-the-art on the TUSZ seizure detection task. Models, benchmark and tools are available at httpt:::////// giigtuttuugg com.
INTRODUCTION
Eleettreenceehhalggraapyy (EEG) remains fundamental diagnostic tool in neurology, yet its clinical power is unlocked only through meticulous manual review of raw waveforms by trained special- ists (Kiloh et al. 2013). This process suffers from critical limitations: it (1) laborious, with experts spending hours reviewing single recording; (2) subjective, leading to significant inter- observer variability; and (3) incomplete, as pragmatic, selective annotation leaves vast amounts of signal information unanalyzed. These challenges create major bottleneck in patient care and moti- vate the need for more effective analytical tools. To facilitate a broader understanding of the clinical context, we provide primer on clinical EEG in Appendix A
The research community's response has evolved from traditional machine learning using hand- crafted features to deep learning models and, most recently, to large-scale self-supervised foun- dation models (Loh et al., 2020; Shocibi et al.) [022]] Babu et al.) (025).. Despite their increasing sophistication, these models share common limitation: they are designed perform specialized classification on isolated tasks like seizure detection or sleep staging, lacking the ability to syn- thesize findings provide a holistic, interpretive analysis. Fundamentally, the field has produced classifiers, but not yet effective interpreters.
The recent success of Large Vision-Language Models (LVLMs)) (Anthropic, 2024 OpenAl 2024
Wu et al. 2024 Bai et al. 2025) offers transformative new paradigm. By treating EEG waveforms as a specialized visual language, we can potentially adapt these powerful models "read" and interpret neurophysiological data with human-like nuance. This approach promises a shift from
"Corresponding author (weilong@sjuu.ccl

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narrow classifiers to comprehensive interpreters. However, critical bottleneck has prevented this leap: the absence of large-scale datasets pairing EEG visualizations with the kind of fine-grained, expert-level interpretations needed for effective instruction tuning.
To this end, we introduce CerebraGloss, an LVLM instruction-tuned for the nuanced interpretation of clinical EEG waveforms. We overcome the data bottleneck by first developing a novel, automated pipeline that programmatically generates a massive corpus of detailed annotations directly from raw EEG signals. Using this unique data engine and Gemini 2.5 Flash (Comanici al.] (025). we create a large-scale instruction dataset and subsequently train CerebraGloss to understand and reason about EEG images. The resulting model the first of its kind, capable of performing not only classification but also generating detailed descriptions of waveforms, artifacts, and background rhythms, and engaging multi-turn, context-aware Billouue-mmmickingg the interpretive process of clinical expert.
Our primary contributions are:
We pioneer new paradigm for EEG analysis that shifts from isolated classification to uni- fied, generative dialogue, enabling a single model to perform multi-faceted interpretation of EEG segments.
We propose novel data generation pipeline where a suite of custom-built analysis tools including pioneering YOLO-based (Redmon et al. 2016) waveform detector is used to programmatically create a large-scale EEG-text instruction dataset.
large vision-language model, demonstrating that with specialized data, a general-purpose L.L.M can be adapted to interpret complex, domain- specific visualizations like clinical EEG waveforms.
We successfully instruction
We construct and release a novel benchmark for comprehensive EEG interpretation, com- prising diverse tasks designed to evaluate model's nuanced understanding beyond single- metric classification.
2 RELATED WORK
Computational Models for Clinical EEG Interpretation. The computational analysis of EEG began with traditional machine learning classifiers (e.g., support vector machine) hand-crafted features (Tzallas et al. 2009; [hhoe 2009 Alickovic et al. 2018). This was followed by deep learn- ing models, such convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which could learn representations directly from data (Supratak et al.) 2017] Chen et al.) 2018 Qiu al. 2023). More recently, foundation models for EEG have emerged, pre-trained on large-scale unlabeled data using self- self-supervieed objectives like BERT-style (Devlin et al., 2019) masked signal modeling (Zhang et al. [223a]] Jiang al. 2024 Wang al. (024) or GPT-style (Radford al. 2018) autoregressive prediction (Cui et al. 2024). While powerful, these models are predominantly evaluated as specialized classifiers for tasks spanning both clinical applications and broader BCI domains (e.g., emotion recognition and motor imagery), lacking the holistic, interpretive capability of human expert.
Bridging EEG and Language. Initial efforts to connect EEG and language have primarily fol- lowed two paths. The first category aims to learn powerful EEG representations for classification by aligning signals with text. This includes methods such as ELM-MIL (Gijsen & Ritter 2025) and EEG-CLIP (Camaret Ndir al., 2025) that perform coarse-grained alignment between multi-hour recordings and summary-level chinccal reports. By design, these approaches are not optimized for grounding textual descriptions to specific waveform events, which central to our generative fo- cus. The second path explores instruction tuning, where models like NeuroLM (Jiang et al.) (025) reframe classification tasks into a multiple-choice format. While innovative, this method is funda- mentally non-generative, precluding free-form output or dialogue. It is also crucial to distinguish our task instr-intrrrening the EEG signal for its clinical significaace -froffoommoo the separate field of brain- to-text decoding Mishra et al. 2025), which aims to reconstruct a user's 's internal speech. Given the limitations of prior work, a model capable of fine fine-graing grained, generative, and conversational interpre- tation of clinical EEG thus remains an open challenge.
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Domain-Specific Post-Training for LVLMs. Domain-specific post-training adapts general- purpose LVLMs to specialized fields like food (Mohbat Zaki 2024 Yin et al. 2025), biomedicine (Li et al. 2023 Zhang et al. 2023b Chen et al., 20244) and remote sensing Zhang et al. 2024). This process typically involves two-stage training pipeline. In the first stage, the model undergoes preliminary alignment using large corpus of domain-sppeiiii image-caption pairs to learn fundamental visual concepts and terminology. In the second stage, the model is fine-tuned on more complex visual instruction datasets cultivate advanced reasoning and instruction-followingg abilities. The creation of these instruction datasets includes applying manual rules (Mohbat & Zaki, 2024). or leveraging powerful teacher models like GPT-4 (Achiam et 2023) to synthesize diverse Chen et al. 2024).
conversational and -aastiinnannwwring data (Li et al. 2023
EEG INSTRUCTION DATA GENERATION
3.1 AUTOMATED PIPELINE FOR STRUCTURED ANNOTATION
The foundation of CerebraGloss is large-scale instruction dataset. As manual annotation of detailed EEG interpretations is prohibitively expensive, we designed automated pipeline which takes raw multi-channel EEG signals as input and programmatically generates a set structured clinical annotations. This pipeline serves as a "data engine", comprising three core modules for identifying significant waveform events, characterizing background activity, and detecting artifacts.
Key Waveform Event Detection. Central to EEG interpretation is the identification of specific, transient graphoelements. To automate this process, we developed CerebraGloss- YOLO,, be- spoke object detection model tailored for localizing and classifying salient events within multi- channel time-series data. is designed to recognize nine climically critical waveform types: spikes, sharp waves, saakke/sharp-ad-sloow-w-w noise, positive sharp transients (blinks), positive and negative square waves (lateral eye movements). Visual examples of these waveforms are provided in Figure Our team of trained annotators under- took an extensive, multi-month labeling process, meticulously curating a dataset from public corpora including DREAMS (Devuyst 2005) and select subsets of the TUH EEG Corpus (Obeid & Picone, 2016), in addition to our private in-house collection. This effort produced a dense dataset of 46,258 expert-labeled bounding boxes across 2,849 unique 10-second EEG segments. The architecture and implementation details of CerebraGloss- YOLO are provided in Appendix B
complexes, K-complexes, sleep spindles, high- high-frequency
Background Rhythm Characterization. Beyond discrete events, the pipeline assesses global background characteristics. It quantifies amplitude as half the peak-to-peak voltage and deter- mines the dominant frequency by first identifying the canonical frequency band (i.e., delta, theta, alpha, beta, and gamma) with the highest power spectral density, and then extracting the frequency with the peak magnitude within that band.
Artifact Identification. To ensure robust analysis, the pipeline incorporates a module identify common artifacts based on their statistical and morphological signatures. This module identifies physiological artifacts, such as muscle activity (EMG) via high- high-frequency power, eye movements (EOG) through spatial correlation patterns in frontal channels, and respiration by its rhythmic slow- wave morphology. It also flags non-physiologice artifacts: electrode noise is identified by a compos- ite criterion of extreme local amplitude combined with loss of correlation with adjacent channels, while flat lines are marked by periods of near-zero signal variance, which may indicate either an artifact clinically significant low-voltage state.
3.2 INSTRUCTION- DATA GENERATION
Leveraging the structured annotations from our pipeline, we constructed a large-scale, multi- format instruction-followingg dataset. We sourced a total of 1.4M 10-second EEG segments (3,889 hours), including the training sets of TUAB (Lopez et al. 2015). TUEV (Harati et al.) 2015). and TUSZ (hah al. 2018), the entirety of TUAR (Buckwalter et al. 2021), TUEP (eeloo et al. 2017 and TUSL (von Weltin et al.) 2017) (which do not provide train/test splits), along with DREAMS (Devuyst] 2005) and the first 100 subjects from HMC (Alverez- & Rijsman 2021
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State
F4-M1 1
NREM2
C4-M1
Additional Annotation
None
02-M1
Frequency & Amplitude
2 arrays with shape (4,10)
C3-M2
Artifact Detector Boxes
2
5
7
10
None
Time (8)
YOLO Boxes
spindle: [['F4-MII, 3.9, 4.7J. ['C4-MI', 3.9.11....94..7. 3.7, ('O2-M1', 3.9, <omittedd
Rule-Based Caption
The channel layout contains C3, C4, F4, 02 channels in 19 standard 10-20 system...Comitee.. <omitted
Rule-Based Multiple-Choie Question
What sleep stage does this PSG segment correspond to?
A) Wake ... >omitedd>>
Description
This segment is recorded from limited channel set (C3, C4, F4, O2) of the 10-20 system <omitted Complex Multiple-Choic Question
Which of the following best describes the key electrographic features seen this 10-second epoch? A) A well-formed sleep spindle and K-complex. <omitted]>>>>
Conversation
User: What your initial observations from this 10-second EEG segment?
Agent: Certainly. This clear unremarkable segment NREM2 sleep. <omitite
One example to illustrate the instruction-following data. "State" and "Additional Anno- tation" are provided by original dataset or our annotators. Meanwhile, our "data engine" detects background, artifacts and waveform events. Using these raw materials, captions and simple QA pairs are generated with rules. Finally, all materials except for the two background arrays and the simple QA fed the LLM,, resulting in three types of instruction-folloiin data. Note that the visual image is not used anywhere the process; we only show here as reference.
Figure I:
2022). From this extensive pool, we generated instruction data through two-pronged strategy: a systematic rule-based approach followed by augmentation with large language model.
Rule-Based Generation. We first employed programmatic approach to generate foundational set of detailed captions and simple question-answer pairs. The template-driven captions synthesize a comprehensive description covering five key aspects: (1) montage configuration, (2) artifacts (e.g., blinks, high-frequency noise), (3) sleep-related events (e.g., K-complexes, spindles), (4) epilepti- form activity, including an assessment of dipole characteristics, and (5) background characteristics, detecting posterior dominant rhythm, paroxysmal delta rhythm and any spatial asymmetries tem- poral variations. To mitigate the inaccuracies introduced by automated identification, strategies such event priority masking, spatial pruning of isolated events and the inductive integration of event groups are employed. Concurrently, we generated multiple-choiee and binary questions to probe for specific knowledge across key domains like artifact presence, sleep staging, and seizure detection.
LLM-PPoered Data Augmentation. To elevate the complexity and conversational nature of our dataset, we utilized Gemini 2.5 Flash (Comanici et al. 2025) -chosen for its optimal balance of capability and cost-as teacher model. We provided the model with the rule-based captions, the -OLLLOOSS-YLOO
bounding boxes from CerebraGloss- YOLO and our artifact detectors, the sleep stage and additional annotation as input. Using meticulously engineered one-shot prompts, with distinct sets tailored for sleep and epileptic seizure data, we guided the model not only to generate rich mixture of instruction types but also to constrain its output to the provided context, thereby mitigating the risk of factual inaccuracies. This process yielded a final dataset of 94K high-quality examples, balanced in 1:1:1 ratio across three formats: (1) Description: a comprehensive. free-text interpretation; (2)
Complex Multiple-Choiee Question: multi-choie questions requiring deeper reasoning; and (3) Conversation: conversational exchanges mimicking a clinical consultation. Examples of each data type are shown in Figure and the prompts detailed in Appendix [
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Stage
Stage
Task Formats: Generative (Caption, Dialog) Interrogative (QA, MCQ)
EEG Coneept Alignment
EEG Instruction- Tunnng
B
Knowledge Scope:
-4 Hours
-44 Hours epoch on 294K
Specialized (Clinical EEG)
1 epoch on 1,96M
General (Visual World)
Figure 2: The two-stage training pipeline for CerebraGiloss. A mix of EEG and gemeral-domain data is used to mitigate catastrophic forgetting. Stage I aligns EEG visual concepts in under one epoch via early stopping, while Stage fine-tunes for instruction-folloving. The process yields specialized model capable of diverse generative and interrogative EEG interpretation tasks.
METHODOLOGY
To endow general-purpose LVLM with the specialized ability to interpret clinical EEG waveforms, we perform continued post-training on the Qwen2.5-VL-3B model (Bai et al. 2025), specifically selected to leverage its robust pre-trained visual representations as a foundation for medical domain adaptation. Our primary goal is to instill fine-grainee EEG understanding while preserving the model's s extensive pre-trained knowledge of general visual concepts. We retain the original model architecture, which consists of a visual encoder, an LLM decoder, and a projector that bridges the two modalities. Our training curriculum for CerebraGloss follows a two-stage strategy, designed to first establish foundational understanding of EEG concepts and then cultivate advanced instruction- following and reasoning capabilities. This process is illustrated Figure 2 and the model structure is detailed in Appendix G
Stage 1: EEG Concept Feature Alignment. The initial stage aims to align the visual features of EEG waveforms with their corresponding semantic concepts in the language model's embedding space. To achieve this while safeguarding the model's pre-existing abilities, we curate blended dataset. This includes 1,4M EEG image-caption pairs generated programmatically by our data en- gine, combined with the 558K general-domain image-caption pairs from the LLaVA Visual Instruct Pretrain LCS-558K (Liu et al. 2024a). During this stage, we freeze the parameters of both the visual encoder and the LLM decoder, exclusively performing full parrameer fine-tuning on the projector. This targeted approach efficiently teaches the model the new visual vocabulary of EEG without risking catastrophic forgetting. We employ an early stopping strategy, concluding the training phase before the convergence point of the training loss curve, thereby preventing overfitting and optimizing training time.
Stage EEG Enstruction-unnnng The second stage focuses on developing the model's abil- ity to follow complex instructions, generate nuanced interpretations, and engage in conversational dialogue. For this, we construct a diverse mnstruction-followie specific and general-purposs examples. The HEG-specific data includes 100K rule-based multiple- choice questions (containing 39K TUSZ seizure questions and 40K HMC sleep staging questions), 94K instruction samples (covering multi-turn conversations, detailed descriptions, and complex rea- soning questions) generated by Gemini 2.5 Flash, and an additional 50K rule-based captions from the HMC and TUSZ datasets. To maintain general instruction-following capabilities, we supple- ment this with 50K general-domain samples, consisting of 30K from the CoSyn-400K (Yang et al., 2025) and 20K from the -LaVV--Insruuct 150K (Liu al., 2023). In this stage, we freeze the visual encoder and perform full-parameter fine-tuning on both the LLM decoder and the projector. The model trained for single epoch on this combined dataset.
dataset comprising both domain-
Implementation Details. We conducted all training experiments on a cluster of NVIDIA A800 (80GB) GPUs We employed the Adam W optimizer with an effective batch size of 256, achieved through gradient accumulation. The learning rate was managed by a cosine scheduler with a peak value of x 1 and warmup ratio of 0. The entire two-stage training process is highly efficient; with the early stopping strategy in the first stage, each stage was completed in approximately 4 hours. More information about instructions and data format of training detailed in Appendix
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5 CEREBRAGLOSS-BENCCH A BENCHMARK FOR NUANCED EEG INTERPRETATION
Existing clinical EEG benchmarks are limited to closed-set classification tasks, such as seizure de- tection in TUSZ (Shah et al. 2018) sleep staging in HMC (Alvarez-Estevez & Rijsman 202T] 2022). While valuable, this paradigm is insufficient for evaluating nuanced interpretation. Specifi- cally, this single-label approach creates label-granularis mismatch by incorrectly propagating file- level labels to every segment, oversimplifies complex signals that may contain multiple co-oct co-occurring events, and ignores crucial context dontext-dependency where waveform's 's meaning changes with patient state. A detailed discussion of these issues is provided in Appendix
To address these limitations and rigorously evaluate model's is ability to "read" EEG, we introduce and will pub- licly release CerebraGloss-Bende it is the first benchmark designed for open-ended clini- cal EEG interpretation and multi-class waveform object detection. CerebraGloss- Bench comprises 90 challeng- ing 10-second segments of full 19-chamnel 10-20 system EEG. Each segment is paired with four-part evalua- tion suite: a free-text description, complex multiple- choice question (MCQ), conversational question- answer pair (QA), and dense, channel-level bounding box annotations for nine critical waveform types (de- tailed Be.).on(.I).. The textual data was initially gener- ated using a programmatic prompting strategy and subse- quently reviewed, edited, and validated by clinical experts ensure high quality and accuracy. All data was sourced from a private in-house collection, with subjects entirely disjoint from those used in our training data to prevent data leakage and ensure fair evaluation. The benchmark offers comprehensive cover- age of clinically relevant phenomena, spanning four major categories and seventeen sub- sub-categories: background rhythms (alpha rhythm, temporal variation, spatial asymmetry, slowing, fast activity, low voltage), artifacts (eye-related, severe artifact, high-frequency noise, chewing), sleep patterns (K-complexes, drowsing slow activity, sleep spindles, delta activity in deep sleep), and epileptiform complexes). The distribution of these
severe
eye
To our knowledge,
alpha
variation
high freq
asy mimetry
artifact
back
thew
ground
slow
Koomplex
fast
drowsing
sleep
fla
epilepsy
4pike-and -wave
spindle
spike
delta
sharp
Figure 3: Distribution of test topics in CerebraGloss- -Bnosh
patterns (sharp waves, spikes, and haakee/harp-and-sloww assessment areas is shown in Figure and examples are presented in Appendix
6 EXPERIMENTS
6.1 QUALITATIVE ANALYSIS
Table shows CerebraGloss following diverse instructions to generate clinically relevant interpreta- tions. correctly identifies the primary feature (a posterior dominant alpha rhythm), localizes tran- sient artifacts, and synthesizes this information coherently, Despite these promising capabilities, the model is not without limitations. We must admit that CerebraGloss still has room for improvement, particularly in mitigating occasional false positives where hallucinates non-existent waveforms challenge likely stemming from noise in our automated data generation pipeline. A detailed discus- sion of noise propagation is presented in Appendix[] Additional results and expert-annotated ground truth can be found in Appendix N
6.2 MAIN RESULTS
We evaluated CerebraGloss against state-of-the-ait multimodal large lan- guage models including specialized biomedical ones on BenebraGloss-Bench. We report accuracy for multiple-choice questions. For open-ended tasks, use ROUGE-1 LLin 2004) to measure factual recall in descriptions and, following prior work (Liu et al. 2023 Li et al. 2023), use GPT- impartial judge for conversational QA. GPT-5 scores the model's ''s response on 1-to- scale across helpfulness, relevance, accuracy, and detail by comparing it against the expert-authored ground truth. As shown in Table 3 CerebraGloss achieves state-of-the-arr perfor-
CerebraGloss-Bent
5 (OpenAl 2025)

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establishing a strong baseline for Published as a conference paper at ICLR 2026
Table CerebraGloss accurately follows the user's instructions. Note that the EEG shown two images to conserve space in the layout. The model is fed the full, single image.
EEG input example, posterior dominant alpha rhythm
FPLAV
FL-AV
F3-A
T3.AV
C3-AV
T5 AV
P3-AV
FZ AV
P4.AV
01-AV
PZ-VV
02:4V
Time (6)
Source: CerebraGlose- img/ 4.av jpg
User
Descrihe this FEG.
CerebraGloss
The channel layout uses standard 10-20 system with an average reference. The overall data quality is good, with only transient eyeblink artifact noted in the frontopolar leads (FPI, FP2) during the initial second. The posterior head region demonstrates well- organized alpha rhythm approximately 10.3 Hz with medium amplitude, suggesting an awake, eyes-closed state. The background activity stable, predominantly composed of low-amplitude alpha band activity at approximately 1 11.8 Hz. No definite epileptiform discharges or physiological sleep waves are observed.
User
What is the primary electrographic feature seen this 10-second segment? A) Generalized background slowing consistent with encephalopathy.
B) A well-organized posterior dominant alpha rhythm.
C) Sleep spindles and K c-omleeees characteristic of NREM2 sleep.
D) Low-voltage, mixed-frequency activity seen during drowsiness.
CerebraGloss
++B. The segment demonstrates a prominent posterior dominant alpha rhythm at ap- proximately 11.0 Hz, which is characteristic of an awake, eyes-closed state.
User
What are your thoughts on this segment?
CerebraGloss
This segment appears be of good quality overall. The primary activity observed is well-organized posterior dominant rhythm approximately 10 Hz, with medium amplitude of around microvolts. This suggests the patient likely in an awake, eyes- closed state.
mance, surpassing even powerful proprietary models like GPT-5. Notably, models such LLaVA- Med (Li et al. 2023) and BioMedGPT (Luo et al., (0024). despite their biomedical focus, struggle significantly on this benchmark. Their training corpora lack paired EEG-text data, rendering them unable to interpret these specialized visualizations. This underscores the critical importance of in- domain instruction tuning and demonstrates the efficacy of our approach.
Waveform Detection. The benchmark's waveform de- tection task evaluates localization and classification. We assessed CerebraGloss- YOLO using mean Average Pre-
Table 2: Waveform detection
Model
mAP@0.5
cision (mAP) at Intersection-over-nnniiin (IoU) of 0.5. As this is the first benchmark for multi-class, channel- wise waveform detection, direct comparisons are unavail- able. CerebraGloss- YOLO achieves a promising mAP (Table this new task.
CerebraGloss- YOLO
40.95%
Standard Clinical Tasks. To validate CerebraGloss established benchmarks, we assessed its performance on two standard clinical classification tasks: seizure detection and sleep staging. For seizure detection, we used TUSZ (Saah et al.) 2018 8), which provides labels for seizure and non- seizure periods. For sleep staging, we utilized the HMC (Alvarez-Estevez Rijsman 2021 2022), which originally contains five stages (Wake, NREMI, NREM2, NREM3, and REM) labeled in 30- second epochs. To align with our model's architecture, we standardized the input by segmenting all recordings into noe-overlgg 10-second epochs. For HMC, each 30-second label was assigned

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Table 3: Instruction-follooinng capability comparison on CerebraGloss-Bent Bench. Multiple-choice questions (MCQ), descriptions, and question-answeriigg (QA) are evaluated using accuracy (%), ROUGE-1 score (%). and GPT-5 score (1-10), respectively. CerebraGloss even outperforms GPT-5. LLa and BioMedGPT cannot follow instructions for MCQs.
MCQ
Description
QA
LLaVA-Med Li ct al. 2023)
8.87
2.83
BioMedGPT [uo et al.
2024
11.82
1.29
Qwen2.5 2LL2.55-V--322 Bai et al.
2025
37.78
36.90
3.57
Gemini 2.5 Pro Comanici et al. 2025
52.22
37.95
3,86
GPT-5 (OpenA1
2025 2025
70.00
37.07
4.58
CerebraGloss-3B
80.00
44.19
4.76
Table 4: Balanced accuracy (%) on TUSZ and HMC. CerebraGloss significantly outperforms its base model Qwen2.5-VLL and achieves a new SOTA result on TUSZ. ELM-MIL cannot be tested on HMC due to its montage setting.
Model
Type
Multi-task
TUSZ
HMC
EEGNet (Lawhern al.
2018
DL
X
65.53
58.51
CNN-Transtormer PPch et al
2022
DL
75.53
68.35
ELM-MIL Gijsen Ritter
2025
DL+ML
78.27
LaBraM (Jiang ct al.
2024
LEM
77.48
68.92
Gram (Li et al. 2025
LEM
78.29
69.97
LLaVA-Med (Li al. 2023)
LVLM
50.00
25.00
BioMedGPT (Luo et 2024
LVLM
50.00
25.00
Qwen2.5-VL-31 (Bar et al. 2025)
LVLM
55.02
25.00
CerebraGloss-33
LVLM
79.21
62.02
to the three corresponding 10-second segments. Furthermore, since the definitive criteria for REM sleep rely on electronyographh (EMG) and electrooculograpply (EOG) signals- not used by our model we excluded the REM stage, formulating the task four-class classification. To ensure a rigorous evaluation with no subject overlap from the training set, we used the official evaluation split of TUSZ (46,091 samples) and the final 26 subjects from HMC (60,678 samples).
We benchmarked CerebraGloss against classic deep learning (DL) architectures, state-of-the-art large EEG models (LEMs), LVLMs and the most recent work ELM-MIL that combines EEG and clinical report. Given the sigmificant class imbalance in both datasets, we report balanced accuracy as the primary evaluation metric. The results are summarized Table CerebraGloss achieves new state-of-the tte--art the TUSZ. seizure detection task, outperforming all specialized models. In contrast, the LVLMS demonstrate negligible performance, often defaulting to repetitive answers and thus achieving only chance-level accuracy. On the HMC sleep staging task, CerebraGloss's 's perfor- mance is competitive yet falls slightly below the top-performing LEM. We posit this discrepancy is less limitation of our model's interpretive ability and more reflection of the task's specific demands. Clinical sleep staging often requires temporal context spanning several minutes to resolve ambiguities. Specialized models, designed to excel at this singular task, may be highly tuned to subtle, short-segment patterns that help distinguish between similar sleep stages. CerebraGlooss, in contrast, is trained for broader, more descriptive interpretation, which may naturally de-emphasize optimization for single, context-poor classification task. A more detailed analysis for HMC is presented in Appendix
General Capabilities. In addition to specialized clinical performance, we evaluated whether our fine-tuning process compromises the model's general vasion-language abilities. We assessed CerebraGloss-33 3B on the comprehensive MMBench (Liu et al.] (024b) benchmark and found that it retains its core capabilities with only marginal performance decrease compared to the origi- nal Qwen2.5-VL. VVL-BB, demonstrating that our approach successfully avoids significant catastrophic forgetting. The detailed results and analysis provided in Appendix
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Table Ablation studies on training configuration and model scale. evaluates the impact of train- ing duration, Stage 2 data composition (1 epoch but without captions or without -LM-augmented data), and model size.
Model Variants
Clinical
Tasks HMC
CerebraGloss-Becch Description
Params
Stage
Stage 2
TUSZ
MCQ
QA
3B
79.68
62.24
78.89
41.11
4.57
3B
0.05
79.21
62.02
80.00
44.19
4.76
3B
0.10
1
79.83
61.46
76.67
41.03
4.40
3B
0.20
79.23
61.16
74.44
41.69
4.30
3B
0.05
54.36
24.09
37.78
22.08
2.67
3B
0.05
0.04
53.32
29.95
51.11
42.66
3.13
3B
0.05
0.25
80.03
56.26
76.66
40.10
4.22
3B
0.05
0.50
78.66
60.74
77.78
43.84
4.40
3B
0.05
w/o cap
78.73
61.80
78.89
51.09
4.58
3B
0.05
w/o aug
78.39
61.29
47.78
9.02
2.34
7B
0.06
80.21
63.34
81.11
44.23
4.64
6.3 ABLATION STUDIES
We conducted a series ablation studies to validate our key design choices, including the training data configuration and model scale. All results presented in Table
Impact of Stage Feature Alignment. We first investigated the impact of the Stage feature alignment by comparing four checkpoints: skipping Stage entirely (0 epochs), an early underfitting point (0.05 epochs, see Appendix D). the training loss elbow point (0.1 epochs), and near-overfitting point (0.2 epochs). While performance on the TUSZ and HMC classification tasks remains compa- rable across all settings, the model trained the underfitting point (0.05 epochs) achieves the best sults on all three generative GerosraGloss-Benchh tasks. We hypothesize that this early checkpoint is optimal because it allows the model acquire the essential visual vocabulary of EEG without over- writing its powerful, pre-existing reasoning capabilities. Further training our programmatically generated, template-heare captions may introduce a "deseriptive bias", which hinders performance on more complex, open-ended reasoning tasks.
Impact of Stage 2 Data Composition. With the optimal Stage 1 configuration, we observed that model performance scaled positively with the amount of Stage 2 instruction data before converging, confirming the value of our dataset. We further explored the role of data components by removing the S0K rule-based captions from the Stage 2 mixture. This reveals an interesting trade-off: performance on the benchmark description task improved, while scores other tasks decreased. We hypothesize that the simpler, rule-based captions, though of lower quality than the LLM-generated data, act as form of regularization. They anchor the model's understanding feature space, preventing it from over-specializing on the stylistic nuances of the LLM -L.--geeertted text. Removing them allows the model to better mimic the high-quality description style required by the benchmark, but at the cost of the general reasoning capabilities inherited from the base model. Additionally, we ablated the 94K instruction-followiin data generated by Gemini and found that the model loses its open-ended generative ability, defaulting to the MCQ format producing gibberish filled with options. This occurs because the remaining Stage 2 data are almost entirely MCQ- based. However, performance HMC and TUSZ does not decline, their learning relies on rule-generated MCQs.
broader, more fundamental
Impact Model Scale. Finally, we investigated the scalability our approach by applying our optimal training configuration to a larger, 7B parameter version of the model. The 7B model shows a general trend of improvement across the evaluation metrics, enhancing performance on both stan- dard clinical tasks and most aspects of CerebraGloss Bench. Bench.. While observed a minor decrease the QA score, the overall positive scaling confirms that our data generation and training pipeline effective and suggests that performance can be further enhanced by leveraging larger base models.

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7 CONCLUSION
In this work, we introduced CerebraGloss, pioneering LVLM that reframes automated EEG analy- sis from narrow classification to comprehensive, generative interpretation. We overcame the critical data bottleneck by developing novel programmatic pipeline, featuring the CerebraGloss-YOLL detector, to create large-scale instruction- -orllooinn dataset. Through a specialized two-stage train- ing curriculum, CerebraGloss establishes new paradigm for unified EEG analysis via generative dialogue. To evaluate this capability, we also built and released CerebraGloss Bench,, the first bench- mark for open-ended EEG interpretation and multi-class waveform object detection. Our experi- ments show that CerebraGloss not only sets a new state-of- ofte--ofthheart on the TUSZ. seizure detection task but also surpasses powerful proprictary models on our novel interpretive benchmark.
While CerebraGloss establishes a new performance baseline, this work also charts a course for fu- ture research. Our image- approach intentionally mirrors current clinical practice; however, paradigm shift towards direct signal-to-text modeling represents a more ambitious and potentially powerful frontier. This, along with avenues for enhancing our data pipeline, extending temporal reasoning. and ensuring clinical readiness through rigorous validation, constitutes the next wave of challenges. We provide detailed discussion of these future directions in Appendix [L] By open- sourcing our model, benchmark, and tools, we aim to equip the research community with the foun- dational tools to pursue these exciting frontiers and accelerate the development of truly assistive neuro-intelligent systems.
ACKNOWLEDGMENTS
This work was supported in part by grants from Brain Science and Brain-like Intelligence Technology-Nationa. Science and Technology Major Project 20252200218000 National Key Re- search and Development Program China (2024YF33608000, National Natural Science Foun- dation of China (62376158), STI 2030-Major Projects+20222200285500,
Dedical-Engincering In- terdisciplinary Research Foundation of Shanghai Jiao Tong University "Jiao Tong Star" Program (YG2023ZD25, YG2024ZD25 and YG2024QNA03), Shanghai Jiao Tong University 2030 Initia- tive, the Lingang Laboratory (Grant No. LGL-1197), GuangCi Professorship Program of RuiJin Hospital Shanghai Jiao Tong University School of Medicine, and Shanghai Jiao Tong University SCS- SCS-Shanghai Emotionhelper Technology Co., Ltd Joint Laboratory of Affective Brain-Computer Interfaces.
ETHICS STATEMENT
Intended Use and Limitations. We state unequivocally that CerebraGloss and our data engine including CerebraGloss- YOLO are research prototypes, intended strictly for non-commercial, academic purposes. As such, it is not intended for clinical diagnosis, patient care, any real-world medical decision-making. The model is designed to assist researchers analyzing EEG data and spur further investigation into general-purpose neuro-intelligent systems. As with any generative model, CerebraGloss is susceptible to generating factually incorrect information or "hallucinations". This risk is compounded by the fact that was trained data from fully auto- mated pipeline, which, despite its effectiveness, can introduce labeling noise or errors. Therefore, its outputs must be critically reviewed by qualified clinical experts and should never be used substitute for professional medical judgment.
Data Privacy and Governance. Our training data includes both public, de-identified datasets (e.g., TUH, DREAMS, HMC) and a private, in-house data collection. All data from the private collection were fully anonymized and collected under protocols approved by institutional re- view board, with informed consent obtained from all participants. Our newly created benchmark, CerebraGloss Bench, also soureed from this ethically approved and anonymized private collec- tion and contains no personally identifiable information.
Broader Impact. Our immediate goal is to accelerate research by providing powerful, open- source tools for EEG analysis and encourage the computational community to ground their
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innovations in the inherent clinical and neuroscientific value of EEG signals. By releasing model, benchmark, and the data generation engine, we hope to foster a collaborative and transparent research environment. While must reiterate that the current version of CerebraGloss is strictly a research prototype, the long-term vision that motivates this work is the development of reliable Al assistants for neurology. We envision future where such systems could support clinicians by automating routine analysis, highlighting potential areas of concern for expert review, and reducing inter-observer variability in EEG interpretation.
REPRODUCIBILITY STATEMENT
The code for data engine, the model weights for CerebraGloss, and the complete CerebraGloss- Bench are released at https: /ttt::://gitubb.../ coom/iewws http://wwwweeeeeaaallooooo/
Details of our two- described in Section 4 The architecture
stage training methodology, including hyperparameters, of CerebraGloss- YOLO is detailed in Appendix We have chosen to release our data engine rather than the specific 1.4M generated data instances, mainly because our data was derived from publicly available sources (listed Section 3.2). Releasing the engine empowers other researchers to apply pipeline their own private specific EEG collections, granting them full control over the data generation process and its outputs.
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A CLINICAL EEG PRIMER
A.1 EEG ACQUISITION AND SIGNAL REPRESENTATION
Electroenceppaalgrrphhy (EEG) non-invasive neurophysiological technique that measures the electrical activity of the brain via electrodes placed on the scalp. The resulting data is multi- channel time-series signal, where each channel represents the voltage difference between two points over time. This high temporal resolution makes EEG an invaluable tool for capturing transient neural events.
Recording Setups: The most common standard for clinical and research applications is the Inter- national 10-20 System, which provides a standardized method for placing 19 recording electrodes and 2 reference electrodes across the scalp, illustrated in Figure 4 Another common setup is Polysommography (PSG), or a sleep study, which typically uses a smaller subset of EEG channels (e.g., central and occipital) alongside other physiological sensors to monitor sleep.
Key Signal Processing Concepts: Raw EEG signals are typically pre-processed before analysis. This involves filtering to isolate the relevant frequency spectrum (e.g., with 1.6-70 Hz band-pass filter) and eliminate specific environmental noise, such as 50 Hz or 60 Hz power-line interference using notch filter. The signal is then often downsampled Hz) to reduce computational load. Two other critical concepts are montage and re-referencing. A montage the specific combination of channels displayed for visual review. Re-referencing the computational process of subtracting the signal from one or more reference electrodes from all other electrodes. This is crucial for mitigating widespread noise and highlighting focal brain activity.
a lower sampling rate (e.g., 200
Our Approach: All raw EEG signals are first band-pass filtered between 1.6-70 Hz and notch- filtered 50/60 Hz. The signals are then downsampled to 200 Hz. For full 19-channel recordings from the 10-20 system, we employ average reference. For the few-channel EEG data from PSG, we use standard ear or mastoid reference (e.g., C3-A2). This scheme is critical for our model as it provides consistent polarity representation for key events across the scalp. For instance, widespread artifacts like eye blinks consistently appear as positive deflections frontal channels, while epileptiform discharges are typically represented negative-going waves. This uniformity simplifies the feature space, allowing the model to more easily learn the spatial signatures different events, task complicated by other referencing schemes (e.g., bipolar) where polarity can reverse between adjacent channels. Additionally, a ten-second non-overlapping division scheme is adopted for all the samples we in the study.
NASION
Fp2
F4
A2
T4
15
16
01
INION
Figure 4: The International 10-20 System. The diagram illustrates the standardized placement the 19 recording electrodes and two reference electrodes (A1 and A2) that constitute the canonical 10- 20 system. The electrode names correspond to their scalp location: Fp (Frontopolar), (Frontal), (Central), Т (Temporal), P (Parietal), and O (Occipital). We emphasize this formal definition, the term "10-20 system" is sometimes inaccurately used in the literature to describe various electrode subsets derived from the higher-density 10-10 system.
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"yy
hy
(a) Spike
(b) Sharp
(c) Spike-ande and wave
(d) Sleep Spindle
Whn W hu
Fp1
F7
W
Fp2
F8
Am
1s
(e) Eye Blink
(I) Eye Rotation
(g) Muscle Artifact
(h) K-complex
Figure 5: A visual vocabulary of key EEG waveforms. This figure displays snippets of common patterns encountered in clinical EEG interpretation. The temporal scales of the eight subplots remain uniform, while the amplitude scales have been adjusted for enhanced display clarity.
A.2 THE "VOCABULARY" OF EEG: KEY WAVEFORMS AND PATTERNS
Clinical experts typically evaluate an EEG by analyzing its background rhythms, identifying key graphoelements (significant waveforms), and distinguishing them from artifacts. To illustrate the key patterns our model is trained to interpret, we present a collection of representative waveform snippets in Figure 5
Background Rhythms: The ongoing background activity of the EEG is characterized by several frequency bands, each associated with different brain states:
Delta (6, 0..-3.55 Hz):
Predominant during deep sleep in adults or indicative of brain injury. Associated with drowsiness, light sleep, and some cognitive processes. The hallmark of relaxed, wakeful state with eyes closed, typically strongest over the posterior regions.
Theta (0, 4-7.5 Hz):
Alpha (a, 8-13 Hz):
Beta (3, 14-30 Hz): Common in alert, active, or anxious state, and can be induced by certain medications.
Gamma (7, >30 Hz). With little interest.
Clinically Signiticant Graphoelements: These are distinct, transient waveforms that hold signifi- cant diagnostic value.
Epileptiform Discharges: These are the primary markers for a predisposition to seizures. They are transient events that stand out from the background activity. Key examples include spikes, which are very brief, high-amplitude potentials (Figure [aa; sharp waves, which have similar morphology but slightly longer duration (Figure 5b); and spike-and-wav complexes or sharp-and and-wave wave complexes, where spike or a sharp is immediately followed by slow wave (Figure (c).
Specific waveforms are the hallmarks of different sleep stages. The defin- ing features of Non-Rapid Eye Movement stage 2 (NREM2) sleep include sleep spindles, which are characteristic bursts of 11-14 Hz activity (Figure and K-complexes, which are large, biphasic slow waves followed by sleep spindles (Figure 5h).
Sleep Patterns:
Common Artifacts: major challenge EEG interpretation is distinguishing true neural signals from artifacts, which are non-cerebral electrical potentials. Our model must learn to differentiate true signals from common contaminants such as eye blinks, which manifest as high-amplitude, syn- chronous vertical deflections in frontal channels (Figure 5e), and lateral eye movements, which pro-
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Figure 6: The CerebraGloss-YOLo structure. CerebraGloss-YOLO follows Backbone-Neck- Head paradigm. After being padded and augmented, EEG data will go into the backbone, which is composed of inception modules (Incept), 1D convolutional layer (Conv), ID dilated convolu- tional layer (DConv), pointwise convolutional layer (IxIC) and self-attention layer (Att)) to catch temporal and spatial information. Later, the neck will employ upsampling (Upx2) to fuse informa- tion from deeper layers to earlier layers. Finally, prediction heads will transform the feature map into a final prediction tensor, which in the shape of (19 channels. S grid cells, B anchor boxes, 3+class number). where 3 is the confidence score, displacement and scaling parameters of anchor boxes. Note that batch normalization and ReLU activation are omitted for simplicity.
duce opposing slow waves in channels on opposite sides of the head (Figure 5t) Musele activity is another frequent artifact, contaminating the signal with high-frequency, irregular noise (Figure 5g).
B CEREBRAGI.OSS- YOLO: A CHANNEL.WWISE DETECTOR FOR RAW EEG WAVEFORM DETECTION
B.I MODEL ARCHITECTRRE
Detecting transient waveforms in raw multi-channel EEG is fundamentally a one-dimensional object detection task. However, this approach has been largely unexplored, primarily due to the scarcity of large-scale, densely annotated datasets required for training such models. The design of such detector must address several unique challenges of EEG data: (1) events are localized to specific channels, requiring per-channel predictions: (2) channels possess spatial relationship defined by the electrode montage, which contains clinically relevant information; and (3) events occur across a wide range of time scales, from brief spikes (70 ms) to persistent artifacts like high-frequency noise that can span the entire window. To address these issues, we developed CerebraGloss- YOLOO (Figure 6), bespoke detector inspired by YOLOv3(Redmo)) & Farhadi 2018). Examples are shown in Figure
Backbone Network. The backbone is responsible for extracting a hierarchy of features from the input signal. To capture waveform features at multiple time scales, we employ an inception mod- ule (Szegedy et al. 2015). which uses parallel branches with different 1D convolutional kernel includincluding dilated convolutions- learn representations of both short- and long-duration events simultancously. To move beyond treating channels as independent streams and explicitly model their spatial topology, we introduce self-attention module (Vaswaní al.) 2017). which treats the feature vector of each channel as token in sequence. By adding learnable position embedding, we inject prior knowledge of each electrode's spatial location into the model. The self- attention mechanism then allows the model to dynamically weight and aggregate information from other channels, while an attention mask ensures can gracefully handle missing or padded channels.
Feature Pyramid Neck. Given that BEG graphoelements exhibit significant duration variability, we employ a Feature Pyramid Network (Lin et al. 2017) to create robust, multi-scale feature repre- sentations. The Neck takes feature maps from multiple stages of the backbone and fuses them via top-down pathway with lateral connections. This process combines high-level semantic information from deeper layers with high-resolution temporal information from earlier layers. The output is
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feature pyramid where each level has different temporal resolution, making the model adept detecting events of varying lengths.
Prediction Heads. A simple prediction head, composed of single pointwise convolution layer, can be attached to any level the feature pyramid. This head transforms the feature map from the neck into final prediction tensor. This tensor encodes, for each channel, temporal grid cell, and predefined anchor box, the necessary parameters for detection: bounding box coordinate offsets, an objectness confidence score, and classification logits for the nine target waveform classes.
B.2 TRAINING AND IMPLEMENTATION DETAILS
Data Preprocessing and Standardization. All EEG signals used for training were segmented into 10-second clips, sampled at 200 Hz, resulting in an input tensor dimension of (C, 2000), where is the number of channels. To handle variability in recording montages, we standardized all samples to the 19-channel 10-20 international system. For recordings with fewer channels, missing channels were zero- pero-padded; for those with more, only the standard 19 were used. Finally, each channel was independently normalized using z-score transformation.
Data Augmentation. To improve model generalization and robustness signal variations, we applied series of augmentations during training. Standard time-series augmentations included the addition of Gaussian noise, random amplitude scaling, and random temporal circular shifts. We also employed two EEG-specific augmentations: (I) random channel dropout, where a subset of channels is zeroed out to simulate poor electrode contact, and (2) random channel permutation, where the physical order of channels in the input tensor is shuffled. This latter technique is strong regularizer that forces the model to rely on its learned channel positional embeddings to understand spatial relationships, rather than memorizing fixed input order.
Anchor Design and Loss Function. Our model employs a set of predefined ID anchor boxes to detect events of varying durations. To match specific events to appropriate feature resolutions, we placed two shorter anchors (0.45 and 1.5 s) on the highest-resolution feature map and one longer anchor (9.5 s) on the lowest-resolution feature map. While our architecture supports predictions at all pyramid levels, we empirically found this sparse configuration offered the best trade-off between performance and computational cost, adding prediction heads to intermediate levels did not yield significant gains. During training, each ground-truth box is assigned to the anchor with the highest 1D Intersection-over- Union (IoU). The model is optimized using composite loss function, standard in YOLO-based models. It consists of a mean squared error (MSE) loss for bounding box coordinate regression and binary cross-entropy (BCE) losses for the objectness score and class predictions. To balance these components, we apply distinct weights: the coordinate loss (Accord) is heavily up- weighted prioritize accurate localization; the objectness loss for anchors containing ground- truth object (Avbj) is also boosted; and the objectness loss for background anchors (Drooty) is down- weighted to prevent the vast number of negative examples from overwhelming the training signal.
CerebraGloss-YOOe was trained for 80 epochs using the Adam optimizer with learning rate lc-4 and a batch size of 32. The specific loss weights were set to Accord 10, Xabj 5, and Ancoby = 0.5.
Hyperparameters.
C A CRITICAL DISCUSSION ON EEG DATASETS FOR SEGMENT-LEVEL CLASSIFICATION
Many widely used public EEG datasets, while valuable for developing models for specific appli- cations, present inherent limitations when adapted for general-purpose, segment-level classification on short epochs (c.g., 10 seconds). This appendix elucidates these limitations, which primarily volve around label- booeulatanllartty mismatch, the oversimplification of co-occurring events, and strong dependency external context.
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sharp spike
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Figure 7: CerebraGloss- YOLO can perform channel-wise recognition nine types of waveforms, including sharps, spikes, sharp/spike-and and-w-- complexes (spsw), high frequency noise (hfnoise), eye blinks (eyem), lateral eye movement (eyert, eyer-), K-complexes and sleep spindles.
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(b) Epileptiform Discharge
Source:
wttpp/t//aaaaaaee..a/aa/aa ww..00000000111 .efff.fff... Figure 8: Illustration of the file-level labeling problem. Both 10-second segments from single
TUAB recording globally labeled as "abnormal". The left segment (a) displays only normal back- ground activity, while the right (b) contains distinct epileptiform discharge. This disparity demon- strates how propagating file-level label creates unreliable dataset for segment-level tasks.
C.I THE PROBLEM OF FILE-LEVEL LABELING IN SEGMENT-LEVEL TASKS
Datasets considered: The TUH Abnormal EEG Corpus (TUAB) (Lopez et al. 2015 and The TUH EEG Epilepsy Corpus (TUEP) (Veloso et al. 2017).
Core Issue: These corpora provide single, file-level label for each lengthy recording, which can span from tens of minutes to several hours. A common practice in literature is to apply this global label to every short segment extracted from the recording for classification tasks.
This approach introduces a significant methodological flaw. In recording globally la- beled "abnormal", the vast majority of 10-second segments often contain only normal background rhythms. Propagating the file-level label to each segment creates noise-laden or fundamentally incorrect test set. An example visually demonstrated in Figure Consequently, a model's perfor- mance such test set does not reliably reflect its ability to recognize the morphological features of EEG waveforms, but rather capacity learn spurious correlations.
Analysis:
C.2 THE CHALLENGE OF CO-OCCURRING EVENTS AND SINGLE-LABER SIMPLIFICATION
Datasets considered: The TUH EEG Events Corpus (TUEV) (Harati et al. 2015) and The TUH EEG Artifact Corpus (TUAR) (Buckwalter et al. 2021)
Core Issue: While these datasets offer more granular, event-level annotations, they are often sim- plified into a single-label classification framework for segment-level analysis.
Analysis: This simplification fails to capture the clinical reality where multiple distinct events fre- quently co-occur within single short epoch. For instance, 10-second segment may simultaneously contain epileptiform discharges, eye movement artifacts, and muscle artifacts. Forcing a model to assign single "primary" label to such segment constitutes ill- ill-posed task that discards rich signal information and does not align with the comprehensive nature of clinical EEG interpretation. An example is visually demonstrated in Figure
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Figure 9: segment from the TUEV dataset with a bipolar reference. showing original bound- ing box annotations provided by the dataset including spike and sharp CerebraGloss YOLO "spsw" refers to sharp/spike and slow wave complexes specifically), artifact (arth), and background (bckg). This example demonstrates the multi-label nature of the data, which renders single-label classification insufficient. It also reveals challenges in the ground truth annota- tions, such as ambiguity and omissions.
(spsw, while in
C.3 LIMITATIONS OF CONTEXT-DEPENDENT AND TASK SPECIFICITY
Datasets considered: The TUH EEG Slowing Corpus (TUSL) (von Weltin et al. 2017).
Core Issue: TUSL is a small-scale dataset designed for the specific and challenging task of differ- entiating post-ictal slowing from other forms of background slowing.
The primary limitation here is twofold. First, the classification of post-ictal slowing is heavily context-dependent. The most definitive feature is not the waveform morphology itself but the knowledge an immediately preceding isolated 10-second segment. Even for human experts, distinguishing between post-ictal slowing and other non- specific slowing based on an isolated epoch can be ambiguous.
Analysis:
innuuraiinooootiioon that is absent when analyzing an
Second, our visual inspection of the dataset revealed significant ambiguity in the segment-level la- bels. The labels appear to reflect the broader clinical context the entire recording rather than the specific content of each 10-second segment. For instance, the "seiz" label does not consistently de- note an ictal event within the segment itself. Instead, it can correspond to various patterns, including normal background rhythms (Figure 10) interictal discharges. Similarly, the "slow" label does not always correspond to canonical slowing patterns. This label inconsistency at the segment level makes TUSL unsuitable for benchmarking models fine- enainna waveform features.
C.4 TECHNICAL INCOMPATIBILITY DUE TO MONTAGES
Datasets considered: The CHB-MIT Scalp EEG Database (hoee 2009 and the Sleep-EDF Database (Kemp et al. 2000).
Core Issue: These foundational datasets primarily available in bipolar montage format.
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Source TUSL/edf /WSS/eddf/aaaaallll s001 10001.200 2003_09 wttp//.tcp.ee/aaaaeeeeeeeeeeaeaa
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Figure 10: Two 10-second EEG segments from the TUSL dataset. Although segment (a) is labeled
"slow" and (b) is labeled "seiz". they visually indistinguishable and resemble normal background rhythms. This highlights the challenge of using TUSI. for segment-level classification tasks where
labels expected to reflect waveform morphology directly.
The visual representation of EEG waveforms is fundamentally determined by the chosen montage. Models trained on data from one montage type (e.g., average reference) learn a specific set of visual patterns that do not directly transfer a different montage (e.g., bipolar). Evaluat- ing model on mismatched montage introduces a significant domain shift, making it impossible to disentangle the model's understanding of neurophysiological phenomena from its robustness to stylistic visual changes. Due to this technical incompatibility, we excluded these datasets from our evaluation.
Analysis:
D PROMPTS
We present the system prompt and one-shot example used for generating the multi-turn conver- sational data. The input to Gemini 2.5 Flash includes rule-based caption, bounding boxes from CerebraGloss-YOL.C and our artifact detectors, the designated sleep stage, and any additional an- notations. In contrast to many prior works (Li et al., 2023) Liu et al. 2023), our system prompt is deliberately designed to be highly detailed and complex. This complexity is direct consequence of the multifaceted nature of the input data; for instance, the model must process a potentially large number of bounding boxes whose labels not standard object detection categories. We ob- served that a simplistic prompt often leads the model to focus on irrelevant details and misinterpret the spatial and temporal significance of the bounding boxes. The one-shot example shown below general-purpose template. In addition to this, we employ distinct one-shot examples tailored specifically for sleep and epileptic seizure data. This specialization is necessary because sleep data typically involves only three or four channels and includes sleep stage information, while seizure data is accompanied by explicit annotations indicating the presence seizure. The decision to use single example, rather than few-shot approach, was deliberate trade-off to manage prompt length. Our empirical evaluations confirmed that a comprehensive system prompt, paired with single, well-crafted example, provides sufficient guidance for the model.
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System Prompt for Conversation
Persona
You expert EEG Analysis Assistant. Your is healthcare professional (c-g., s., a neurologist or an EEG technician) who reviewing a 10-second EEG segment and asking for your interpretation. Your tone should be professional, precise, and collaborative.
# Core Task
Your primary goal is to interpret the provided textual EEG data and generate a **2-4 round, logically progressive conversation* between the User and the AT Agent. The conversation must **yythheiize and interpret** the data as if you are viewing clinically relevant information, while intelligently filtering out noise and false positives from automated analysis.
EEG graph, focusing only on the most salient
# Input Data Schema
You will receive 10-sccond EEG context containing to four key-value pairs:
1. {caption)"" An automatically generated summary of the EEG signal.
2. (bboxes) Automatically detected waveform events. 'bboxesl and "bboxes2" should be treated as single, merged set detections.
{state} The patient's 's sleep stage (e.g.. "REM", "NREM2", "Wake").
4. ddescription A manually annotated, high-confidence description of the key findings. This is the most reliable piece of information.
# EEG Terminology Glossary (Your Knowledge Base)
Use this glossary to interpret the "booxes" data correctly.
Epileptic Discharges:**
"sharp". "spike", "spsw" (spike-and and-slow-weel These are potential epileptiform discharges. ***Caveat:** sharp" waves can also be benign variants (e.g., vertex sharp waves) artifacts (e.g., blink-induced). Context is key.
Sleepp Related Waveforms:
"Kcomplex', "spindle': Hallmarks of NREM stage sleep. "coomplex" can also appear in NREM3.
**Mandatory Contextual Check:** As per the information hierarchy, if a {sstate}* is provided, you ** evaluate all waveforms within that context. Confirm if expected waveforms (e.g., present in "(bhoxes)'. Equally important, you note if unex- pected waveforms appear (e.g.. prominent alpha rhythm during "NREM3"). This consistency check a core part of your analysis.
'spindle" "NREM2")
** Artifacts & Noise (To be identified and usually dowaplayed):**
"muscle": Treat as "hfnoise" frequeyry noise), caused by either EMG or poor electrode contact.
'eogv': Treat "eyem" (eye movement). On FP1/FP2, this indicates blinks. unilateral, may indicate dipole.
cog_left', cog_right', "cyer+', "eyer-" Lateral eye movements.
respiration" nan inf" 'lat',, ggoball bad", severe artifact': These are all significant arti- facts often caused by respiration or poor electrode contact. flat' could also indicate low voltage electrocerebral silence, but is usually an artifact in short segments.
# Core Logic& & Prioritization Rules (Your "Thinking" Process)
Follow these steps to analyze the input and structure your response.
##1,
Establish the Ground Truth (Information Hierarchy):**
present, this the definitive finding. Your * **riority (Highest Trust)*** [deseription)"
conversation MUST centered around it.
**Priority 2 (High Trust):** (sstate).. : If present, the sleep stage provides crucial context that frames the entire interpretation. Your analysis of waveforms must be consistent with this state. For example, your primary discussion should be about sleep spindles if the state is "NREM2". or about delta waves the state "NREM3".
**Proritty 3:** (caation)" Use this to get general overview and to corroborate findings.
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**Priority 4 (Lowest Trust):s* [bboxes)". Use this raw evidence to support or refine the findings from the caption and description. Your main job is filter the signal from the noise in "bhoxes" **; For example, bboxes" shows 'sharp' wave at the same time as an nan "naniinf; you MUST interpret it as an artifact, not an epileptiform discharge.
*#2. Analysis Heuristics (How to Interpret):**
**Facus on Prominence: Your conversation should revolve around the most salient electro- graphic features, For example, well-formed posterior dominant rhythm in an awake patient is key finding, just widespread epileptiform discharges are. the recording is heavily contami- nated by artifacts, the poor quality itself is the most salient feature. Only discuss events that are clearly significant. A single, isolated 'sharp' is less important than periodic pattern of spikee waves.
** "SSythhesize, Don't ton't List:** ** Do not just list events from "bboxes". Your value is in connecting the dots. For example, connect the presence of widespread "muscle" artifacts statement about "poor data quality due to muscle activity"..
** Artifact Handling:** If artifacts m'uuscle,, "eemm etc.) are pervasive and obscure the recording, make this the primary point of your first response. If they are minor, mention them briefly needed. Merge fragmented, continuous artifacts (c.g.. "muscle" from 0-5s and 6-10s) into a single statement ("persistent muscle artifact").
*nffrriing Normality:** If no clear epileptiform discharges ('spike", "spsw". 'saarp' a suspicious pattern), widespread artifact, or significant hackground shift, you must infer that the segment unremarkable or within normal limits. In this case, your primary task is to describe the normal, expected features for the given context.
*43. Structure the Conversation (The Narrative Arc):
Follow the appropriate path based on your initial analysis.
**Path A: If Significant Findings Present (Abnormalities Major Artifacts)
R*Ronnd 1: The Big Picture. Start with an assessment of the most salient feature. it poor data quality? Is there clear epileptiform activity? ? (e.g.. "The recording is dominated by artifacts", "There is clear epileptiform activity present").
*ounn 2: Zooming In. In.** The user asks for more detail on the most important finding. Provide specifics about the key waveform: its type, location, frequeney, and morphology.
**Round 3: Clinical Implications. The user asks "What does that mean?". Explain the potential
clinical significance of the finding (e.g.. "This pattern is suggestive of focal seizure onset", "This dipole pattern points towards source in the left hemisphere").
***ound 4 (Optional but Recommended): Context and Caveats, ** Provide concluding state- ment, often disclaimer about the limitations of a short segment. Example: "These findings should be correlated with the full study and the patient's s clinical history."
Path the Segment Normal or Unremarkable
**Round 1: Confirmation of Normality. ** Start by stating that the segment appears within nor- mal limits for the given context (e.g.. patient's state). Brietly describe the key normal features you observe. (e.g.. "This segment appears unremarkable, showing a well organized rior dominant rhythm, consistent with relaxed wakefulness." or "This shows typical features of NREM2 sleep, including well-formed sleep spindles and a K-complex.")
poste-
**Round 2: Specific Exclusion and Conclusion. ** The user asks for confirmation (e.g., "So, nothing to worry about here?"). Your response should explicitly confirm the absence of key ab- normalities and provide concluding statement. (e.g., "Correct.
10-second view, see no epileptiform discharges, focal slowing, or other significant abnormalities. The background appears well-regulated and symmetric.")
Output Format Constraints
Generate a conversation with #*2-4 rounds** based on the information content.
Each round must contain "User" prompt and an AAgent response.
The conversation encouraged to be **logically progressive with each round building the last.
** **ABSOLUTE RULE:** Do *NOT*** mention the terms (captionn) (bboxes) {state) } or {description): in your output. You are interpreting graph, not the data structure provided to you. Your language should be natural and conversational, yet clinically precise.
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One of the One-Shot Examples for Conversation
# Example Input:
caption: The channel layout uses the standard 10-20 system, using average reference. Data qual- ity is poor and the signal is severely contaminated by artifacts. In T5 demonstrates extreme values present during 0.7-3.2s, 3.6-4.9s, 6.7-8.1s, 9.2-9.9s. s. In the whole brain persistently demonstrates high- frequeney noise present throughout the recording period. Frontopolar leads (FP1, FP2) demonstrate eyeblink artifacts during 7.5-7.8s. Consequently, artifact-induced sharp waves are observed 01, 02 during 7.4-7.9s. Periodic sharps and spikes are observed in the left hemisphere and the in parietal and occipital lobes of the right hemisphere and in frontal, central, and parietal lobes midline with approximate frequency 0.5 Hz. suggesting left-right dipole phenomenon.
P3,
bboxesl: { ('muscle": [I'O,-AV', 0.O, 8.9), ['O2-AV", 0.0, 9.8], ('33-VV, 0.0, 10.0), ('T6-AV', 0.O, 10.01. I'CZ-AV", 0.0, 9.9], ('EPI-AV 0.1, [. ("P4-AV", 0.1, 10.0), ('F7-AV", 0.2, 10.0), (VT4-AV", 0.3. 10.0), ['F3-AV", 004, 9.9], I ('F4-AV", 0,4, 9.9], [ ('C4-AV", 0.4, 10.0), ['P3-AV", .0..0,00004 0.4.9.9). ['F8-AV", 0.4, 9.91, ['F7-AV', 0.4, 9.9], ['FP2-AV", 0.5, 7.5], ('P7-AV' 0.6, 9.9], ["C3-AV', 0.9, 9.91, ['FPI- AV", 7.7, 9.01, ['FP2-AV", 7.7, 8.91, ["T5-AV', 9.3, 10.01I, 'nan_inf': [["TS-VV', 0.7, 3.21, ['T5-AV', 3.6, 4.91, L"T5-AV, 6.7, 8.1], ("T5-VV', 9.2, 9.9]], 'cog v': [["PP--V", 7.5, 7.7), [*FP2-AV', 7.5, 7.71I)
bboxes2: ('hfnoise": [['FPI-AV', 0.2. 9.8]. ['FP2-AV', 0.6. 9.4,, ['F7-VV' 0.5, 9.5], ['P--V", 0.3. 9.71. ['F4-AV", 0.2. 9.8], I'T6-AV', 9.71, I'T4-VV' 0.5, 9.5], I'02-AV', 0.4. 9.6], ('F8-AV', O.S, 9.61, ['C4-AV', 0.3, 9.71, "T3-AVV, 0.3, ('T5-VV, 0.5, 9.61, I'CZ-AV', 0.7, 9.4,, I'FZ-AV", 0.5,
9.6], I'O1-AV', 0.5, 9.6], ('F3-VV, 0.4, 9.6), I'T4-VV,, 5.4, 10.0), 1PP3-AV', 0.6, 9.5,, (VPZ-AV", 0,5, 9.61, ('C-AVV, ('C3-AV", 0.6, 9.4II. 'eyem": [['F8-AV', , 2.2, 2.6], ('F2--V 7.5, (TT-AV 2,2, 2.6),
[*FP2-AV', 4.8, 5.1], ['FP2-AV', 2.2, 2.5]. ['T-AVV, 4.7, 5.1], A'FR-AV,, 4.7, S.1], ["FP2-AV', 8.8, 9.11. ['FPI-AV', 4.7, 5.1], ['FPI-AV', 2.3, 2.5]. ["F8-AV'. 8.8, 9.2], ['P4-AV', 448. 5.1], [VF--VV, 2.2, 2.5]. [*F7-AV,, 8.8, 9.]]], 'spike': [['OI-AV', 9.7. 10.0], ('O2-AV", 9.7, 10.0], ['O2-AV', 8.8, O.1 888, 9.1], AV, 977, 10.0). (VFZ-AV", 9.7, 10.0). ('PZ-VV, 22, 2.5], (VPZ-AV., 5.1J, (VFZ-AV,, 8.8, 9.1]. I'FI-AVV, 9.7, 10.0). AVC,-AV', 9.7, 10.0). AV' 9.7, 10.0). I'F3-
9.7. 10.0). I'P4-AV,, 8.8. 9.1]I. 'sharp': PL' 8.8. 9.21. [P3--VV 2.2, 2.6,, ('O1-AV,, 2.2
2.6], ['P3-AV', 5.1], ['O1-AV', 4.15,.11 PP3-VV 7.4, 7.91, C33--VV, 2.2, 2.6,, ['F-AVV. 22,
2.6]. ["P3-AV', 8.7, (CC3-VV' 5.1, ('2-AVV, 222, ,22, 2.5), I'ZZ-VV' 222, 2.6), I'CZ-VV. 48,
(FF-AVV, , 9.2]. ('O1-AV,, 77.4, 7.9). ['PZ-AV',9. 10.0), ['O2-AV,, 7.5 799, ['FZ-AV'.48, 5.8,
5.11. (C3-AV', 8.8, 9.21, ["F3-AV", 4.8, 5.21, (P3-AV', 9.7, 10.0) ['02-AV', 5.1, 17,...V.
state: None
description: None
# Example Output:
**Uerr:* The EEG pattern looks weird. Is it polluted by artifact?
** **ent: Certainly. The first thing note that the data quality is quite poor. The entire recording significantly obscured by high-frequency artifact, and there are extreme values, particularly the left temporal region. However, despite the noise, there appears to an interesting underlying pattern that warrants a closer look.
**User;** I see the artifact. What is the underlying pattern you'le referring to? ** ""ent: Beneath the artifact, there's recurring, generalized pattemn of sharp and spike waves. They appear periodically, approximately every two seconds, consistent with frequency about 0.5 Hz.
**Usrr*** Can you characterize the distribution of those discharges?
** Agent:: ** Yes. The discharges exhibit clear dipole-like field. The negative sharp waves and spikes predominantly in the left hemisphere, combined with positive sharp transients in the right hemisphere. This suggests consistent, organized source for the activity.
**User:** What does that dipole suggest?
** ** A stable dipole pattern like this points towards focal generator. Given the negative po- larity over the left hemisphere, the source of these epileptiform discharges likely located somewhere in that region. Of course, given the significant artifact and the short duration of this clip, this finding should be interpreted with caution and correlated with cleaner portions of the full study.
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E INSTRUCTION-FOLLOWII DATA EXAMPLE
State
F4-M1 y
NREM2
C4-M1
Additional Annotation
None
O2-MI
Frequency & Amplitude
C3-M2
2 arrays with shape (4,10)
Artifact Detector Boxes
2
7
10
None
Time (8)
YOLO Boxes
spindle: [['F4-MI', 3.9, 4.7J, I'C4-MI', 3.9, 4.7J, ['O2-M1', 3.9, 4.7J, I'C3-M2', 3.9, 4.6]], Kcom- plex: [['O2-MI', 6.9, 8.2], ("F4-MI", 6.9, 8.2), ("C4-MI', 6.9, 8.21), eyem: [['F4-M1', 1.3, 1.911
Rule-Based Caption
The channel layout contains C3, C4, F4, O2 channels in 19 standard 10-20 system channels, using mastoid reference. Overall data quality is good. This EEG recording is obtained during sleep. Sleep spindles present in F4, C4, 02 and C3 during 3.9-4.7s. K-complexes present in F4, C4, 02 during 6.8-8.2s. background activity stable, predominantly composed of low-amplitude Theta band (at approximately 6.8 Hz, amplitude about 18.6 (V).
Rule-Based Multiple-Choic Question
What sleep stage does this PSG segment correspond to?
A) Wake
B) NREMI
C) NREM2
D) NREM3
$$C.**
Description
This segment is recorded from limited channel set (C3, C4, F4, 02) of the 10-20 system using a mastoid reference. The data quality is good. This segment of stage NREM2 sleep characterized by the presence of widespread sleep spindles from 3.9-4.7 seconds, followed by K-complex prominent the right-sided channels between 6.8-8.2 seconds. The background activity composed of stable, low-amplitude theta rhythm. The findings are characteristic of and consistent with stage NREM2 sleep. Complex Multiple-Chon Choice Question
Which of the following best describes the key clectrographic features seen in this 10-second epoch? A) well-formed sleep spindle and K-complex.
B) A sustained posterior alpha rhythm indicative relaxed wakefulness.
C) Diffuse high-amplitude slow waves characteristic of NREM3 sleep.
D) Rapid eye movements and low-voltage mixed-frequency background activity.
** *..* ** A well-formed sleep spindle and K-complex. This segment clearly demonstrates both
sleep spindle (around the 4-second mark) and K-complex (around the 7-second mark), which are hallmark features of NREM Stage sleep.
Conversation
User: What your initial observations from this 10-sccond EEG segment?
Agent: Certainly. This a clear and unremarkable segment of NREM2 sleep. It demonstrates classic features, including well-formed, bilaterally synchronous sleep spindles around the 4-second and a K-complex at approximately 7 seconds, all on a stable theta background. User: So, this appears to be normal sleep segment?
Agent: That correct. In this 10-second view, tries, or other abnormalities. The activity well-organized and entirely consistent with normal stage 2 sleep.
epileptiform discharges. significant asymme-
One example illustrate the instraction-followiing data. "State" and "Additional An- notation" are provided by original dataset or our annotators. Meanwhile, our "data engine" detects background, artifacts and waveform events. Using these raw materials, captions and simple QA pairs are generated with rules. Finally, all materials except for the two background arrays and the simple QA are fed the I.M,, resulting three types of eestruction-oolloiing data. Note that the visual image is not used anywhere in the process; we only show it here as reference.
Figure 11:
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F DATA
Instructions for EEG description. The list of instructions used in both stages to describe the EEG content is shown in Figure 12 They present the same meaning with natural language variance. We do not distinguish between detailed descriptions and simplified descriptions, primarily because unlike the first stage of previous LVLMs (Liu et al. 2023 [i et al. 2023) which employed very brief captions, the captions we employ in the first stage are comparable in volume to the descriptions in the second stage.
"What does this EEG show?"
"Describe this EEG."
SSmmmarize the main features of this EEG."
Provide caption for this EEG segment."
"What the overall impression this EEG?"
"Generate description of this EEG."
"How would you caption this ERG?
"Give summary of this EEG pattern."
WWhat happening in this EEG?"
Compose caption for this EEG."
Figure 12: The list of instructions for EEG description.
Data Format of Training. Since the LVLM we are instruction-tun tuuiong is Qwen2.5- VL, we con- struct our instruction-tuning -unnng dataset using the ChatML format, shown in Figure [3 We use the same format in both of the two stages.
<im_start>>user
Ximgg>xxx.jng<//nng What sleep stage does this EEG segment correspond to?<<im_end>>>>
<im start>>assistam
NREM2. can see clear sleep spindles around the 4-second mark, <im_end:
<im_start> user
So. this appears to be normal sleep segment? <im..idd
<imm.tart>-assistee
That correct. epileptiform discharges or other abnormalities can seen. <in_ed>>>>>>>
Figure 13: The dataset format example of ChatMI,, which are used in both stages. Answers and special tokens (blue in the example) are supervised.
Instructions to test on TUSZ and HMC.
Multiple-choice questions are shown in Figure 14
TUSZ
Does this EEG recording show evidence of seizure activity?
A) Yes
B) No
HMC
What sleep stage does this PSG segment correspond to?
A) Wake
B) NREMI
C) NREM2
D) NREM3
Figure 14: The multi-choice questions to test TUSZ and HMC.
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Stage I:
Stage 2:
Concept Alignment
EEG Instruction- Tuning
Qwen2.5 LM
Qwen2.5 LM
Projector
Projector
VIT
VIT
Figure 15: The architecture and the two-stage training pipeline for CerebraGloss. In Stage 1 only the projector tuned, while Stage both the projector and the LLM decoder are tuned.
G MODEL ARCHITECTURE
CerebraGloss adheres the established architecture of modern LVLMs, building upon the Qwen2.5- VL framework. Our approach adapts this powerful, general-purpose foundation to the specialized domain EEG interpretation through targeted fine-tuning strategy. As illustrated Figure 15 the model comprises three core modules:
Visual Encoder (ViT): A pre-trained Vision Transformer (ViT) serves the model's "eyes". Its function is to process the input EEG waveform image, dividing it into grid of patches and converting each patch into high-dimensional feature embedding. This sequence of embeddings numerically represents the visual content of the EEG, capturing spatial and temporal patterns in the waveforms. Throughout our training process, this mod- ule remains frozen to leverage its powerful, pre-existing visual representation capabilities.
Projector:
The projector is lightweight neural network that acts as the crucial bridge between the visual and language modalities. It takes the sequence of visual embeddings produced by the VIT and transforms them into the same embedding space used by the language model. This alignment makes the visual information "intelligible" to the text- based decoder, allowing it to reason about the content of the EEG image.
Large Language Model (Qwen2.5 LM): A pre-trained LLM (in our case, Qwen2.5) func- tions as the model's "brain" and "voice". It is an autoregressive decoder that receives the projected visual features, concatenated with the user's text prompt, and generates the fi- nal textual output word by word. This module is responsible for all high-level reasoning, eestructio-followinng and language generation.
This modular design is central to our two-stage training strategy detailed in Section In Stage we exclusively fine-tune the Projector to efficiently teach the model the new visual vocabulary of EEG. In Stage 2, we fine-tune both the Projector and the Qwen2.5 LM to cultivate advanced, domain-specific reasoning and conversational abilities.
H RETHINKING THE TASK OF AUTOMATED SLEEP STAGING ON HMC
CerebraGloss's performance on the HMC sleep staging task is slightly below the state-of-the-art (SOTA). We investigate whether this stems from insufficient training data or from fundamental lim- itations of the task itself.
The Limited Impact of Increased Training Data. SOTA methods train on the full HMC dataset of over 230K samples, whereas our instruction-tuning stage included only 40K HMC sleep staging questions and 24K related caption samples. To test if this data disparity was the cause, we continued training CerebraGloss-39 on an additional 90K HMC sleep staging questions. This supplemental training yielded only marginal 1% performance increase, with the training loss decreasing very slowly. This result strongly suggests that simply increasing the volume task-specific data is not the key substantial improvement and points towards more inherent issue.
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NREMI
NREM2
NREM3
Wake
NREMI
NREM2
NREMB
NREMS NREME NREMI Wake
1534
1266
80
9057
1627
31
NREME NREME NREMI
2105
2050
4058
391
1235
3734
3543
92
714
1614
19787
4414
246
2172
219
2872
43
3475
10584
18
147
3072
10893
(a) CerebraGloss-3B
(b) SOTA
Figure 16: Confusion matrices for CerebraGloss (a) and the SOTA model (b) on the HMC sleep staging task. Both models show significant confusion between NREMI and NREM2, highlighting a shared difficulty likely rooted in the task's ' inherent ambiguity.
F4-MI
F4-MI
1967
19h
C4-I
C4-MI
02-MI
C3-M2
C3-M2
Time (5)
Time (5)
(a) label: "NREMI"
(b) label: "NREM2"
Source: HMC/ SMMCrecordings SN1 edd
Figure 17: An example of two visually similar 10-second EEG segments from the HMC dataset. De- spite their resemblance, Segment (a) is labeled as NREM1, while Segment (6) labeled as NREM2. This illustrates the inherent ambiguity faced by models when staging isolated, short epochs without broader temporal context.
Inherent Ambiguity in Short-Epoch Sleep Staging. We argue the performance ceiling is rooted the ambiguity of classifying isolated, short EEG epochs. According to AASM standards (Mal- hotra, 2024), sleep stages are identified by key graphoelements. For instance, the onset of NREM2 is defined by the appearance of sleep spindles or K-complexes, while NREMI is characterized by features such the replacement of alpha rhythm with low-amplitude mixed-frequent frequency activity and the presence of vertex sharp waves. However, these markers are stochastic and often absent within single 10-second or 30-second window. This challenge is reflected in the confusion matrices (Figure [6), where both CerebraGloss and the SOTA model show the most significant confusion between NREMI and NREM2 which have similar backgrounds. This is expected outcome, as human experts rely on contextual information from surrounding minutes to stage ambiguous epochs confidently. Models operating on context-stripped segments are deprived of this crucial information, forcing them to classify based on incomplete evidence (see Figure I7 for an example of ambiguous, visually similar segments with different labels).
Implications for Future Research. This analysis suggests that pushing for marginal gains on the HMC benchmark may involve overfitting to subtle statistical cues within isolated epochs rather than developing robust, clinical understanding of sleep architecture. We posit that CerebraGloss'' slight performance deficit is not a weakness but reflection of its training for broader, descriptive single, context-poor classification problem. The more
tasks, which discourages overfitting meaningful path forward is not to optimize for single-epoch classification, but to develop models capable of reasoning over longer temporal contexts. Such approach would better mimic expert clinical practice, and CerebraGloss' 's generative architecture provides a strong foundation for this more ambitious and clinically relevant goal.
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Table Average Precision (AP) of Sooooraloos-YOLL an Interseettion-ove--Uniioo (loU) of 0.5 on the SerebraGloos-Bencd waveform detection task.
Waveform
AP@0.5
Waveform
AP@0.5
Waveform
AP@0.5
spindle
0.71
Kcomplex
0.47
hfnoise
0.75
eyem
0.76
eyer+
0.04
eyer-
0.15
sharp 0.63
spike
0.19
spsw
0.00
Table Error rates of CerebraGloss on CerebraGloss- Bensch MCQs. Note: Major categories (Total) are mutually exclusive. Sub-labels represent subsets and may overlap; thus, their counts do not sum the category total.
Category
Error Rate
Category
Error Rate
Sleep (Total)
1 / 23
Background (Total)
/ 20
spindle Kcomplex
1 / 7
Epilepsy (Total)
6 / 18
0/3
spsw
4
Artifact (Total)
5 29
spike
2 /
eyem, eyer+/-
0/11
sharp
2/
hfnoise
1/8
I ERROR ANALYSIS AND NOISE PROPAGATION
In this section, we analyze how errors and noise from our automated data generation pipeline prop- agate the final CerebraGloss model. We focus on the correlation between the quality of the gen- erated instruction data and the model's performance on the CerebraGloss-Bence Multiple-Choiee Questions (MCQs).
Propagation of Detection Quality. We first examine the relationship between the upstream de- tection quality (from CerebraGloss- (oebraaloss-YOLO) and the downstream interpretation accuracy. Table 6 presents the Average Precision (AP), while Table 7 summarizes the error rates on corresponding MCQs. The detector performs well on common waveforms but struggles with rare or subtle ones.
strong positive correlation observed across both physiological waveforms and detected artifacts. high-performing detection classes, such as sleep spindles (AP 0.71) and eye movements (AP 0.76) consistently correspond to minimal MCQ error rates (14.3% and 0%, respectively). Conversely, classes where the detector struggles, such the spike-and-wave complex (spsw, AP 0.00), result in significantly higher error rates (57.1%) This evidence suggests that the reasoning capability of CerebraGloss is heavily bounded by the precision of the upstream object detection pipeline.
Impact of Pipeline Coverage and OOD Data. The analysis of artifact errors further highlights the critical role of training data coverage. Artifact identification in our pipeline generally employs a hybrid approach combining YOLO detection with statistical rules. The model demonstrates robust performance on eye movements and high-frequency noise (0% and 12.5% error rates), aligning with their strong YOLO performance (AP 0.76 and 0.75) and inclusion in the training set. In stark contrast, the model exhibits 100% error rate (2/2) for chewing artifacts. This failure is attributable the fact that chewing artifacts are neither detected by our YOLO model nor included our instruction generation rules, representing out- disttiibtiittoo (OOD) scenario. This indicates that CerebraGloss cannot effectively zero-sha shot complex, specialized artifacts without explicit supervision from the data engine.
Background Rhythm Characterization. We omit correlation analysis for the Background cate- gory due to a task misalignment between the pipeline's output and the benchmark's evaluation. Our pipeline utilizes Fast Fourier Transform (FFT) to deterministically calculate dominant frequency and amplitude. However, the benchmark MCQs assess qualitative features such as spatial symmetry and temporal variability. Since the pipeline does not explicitly parameterize these qualitative attributes, the errors in this category stem from the model's lack of explicit supervision on these features rather than noise in the frequency calculations.
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2.25
1.8
2.00
Underfiting
1.6
1.75
1.4
Loss
Loss
1.2
1.25
1.0
1.00
Elbow Point
0.8
0.75
Near Overfiting
0.6
0.4
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Epoch
Epoch
(a) Stage
Loss
(h) Stage 2 Loss
Figure 18: Training loss curves for Stage 1 and Stage of CerebraGloss-33 Stage b is trained on the underfit point of Stage (a). which learns the visual vocabulary for EEG without compromising its original strong reasoning skills.
TRAINING Loss
Figure showw the training loss curves for Stage and 2 of CerebraGloss-3B Our ablation exper- iments indicate that training Stage 2 at the underfitting point of Stage yields the best performance. We believe that at this point, the model has not only acquired the visual vocabulary of the new EEG domain but has also preserved its original strong reasoning capabilities. Since caption data is highly structured, further training would cause the model to more easily learn this "stereotyped pat- tern" rather than general "EEG-language" associations. Additionally, is interesting note that the loss value at the underfitting point of Stage is around 1.6, which is consistent with the convergence values of LLa'A's (Liu al. 2023) and Qwen-VIL VL's (Bai al. 2023) pretraining.
K GENERAL TASKS
To ensure that domain-specifer fine-tuning did not result in catastrophic forgetting of the model's general vision-language capabilities, we evaluated CerebraGloss-3B on the comprehensive MM- Bench benchmark (Liu et al. 2024b). MMBBnch assesses wide range of abilities, from object recognition and localization to complex reasoning over 20 distinct sub-tasks. We compared the performance of CerebraGloss-33 against its base model, Swen2.55VL--35 As shown in Table & CerebraGloss-33 achieves an overall score of 84.80% only a marginal 0.55% decrease from the base model's 85.35%. The performance across individual sub-tasks remains highly comparable. These results strongly indicate that our training strategy successfully imparts specialized EEG inter- pretation skills while preserving the model's robust, pre-existing general-purpos abilities.
L FUTURE WORK
Despite its strong performance, CerebraGloss has limitations that open avenues for future work. Its reliance on fully automated data pipeline can introduce noise and lead to factual inaccuracies. Future work should focus on enhancing the precision of this data engine. More fundamentally, our approach treats EEG specialized image, framing the task as a vision-language problem, which does not result in the loss critical information, as doctors also interpret EBGs visually. However, more native and powerful paradigm would be a true EEG-language multimodal model, which requires developing a dedicated EEG encoder capable of directly aligning raw time-series signals with fine- grained textual descriptions, bypassing the intermediate visual representation entirely, Fur- thermore, our current model processes fixed 10-second segments, while clinical interpretation often requires reasoning over longer temporal contexts. Finally, blinded performance studies with expert
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Table 8: Comparison of MMBench evaluation set results for Qwen2.5-VL-3B and CerebraGloss- 3B. All scores are reported in percentage (%).
Category
Qwen2.5-VL-3B
CerebraGloss-3B
Action Recognition
91.16
90.70
Attribute Comparison
78.72 72
78.01
Attribute Recognition
93.56
93.94
Celebrity Recognition
95.45
94.95
Function Reasoning
90.79
89.47
Future Prediction
61.54
59.23
Identity Reasoning
100.00
100.00
Image Emotion
85.50
82.50
Image Quality
63.33
61.33
Image Scene
98.03
98.28
Image Style
93.40
93.40
Image Topic
97.14
97. 14
Nature Relation
84.92
87.71
Object Localization
64.76
65.08
OCR
92.31
93.59
Physical Property Reasoning
73.52
73.52
Physical Relation
65.96
59.57
Social Relation
96.51
95.93
Spatial Relationship
57.63
55.93
Structuralized Image-Tex Understanding
85.46
84.04
Overall
85.35
84.80
neurologists and uncertainty quantification are essential for building trust and ensuring patient safety in any clinical decision application.
M THE USE OF LARGE LANGUAGE MODELS
We utilized Large Language Models (LLMs) as assistive tools in this work and take full respon- sibility for all content. The final manuscript and all codebase underwent meticulous review and validation by the authors. The roles of LLMMs are detailed below.
Data Generation and Evaluation. We employed Gemini 2.5 Flash transform our programmat- ically generated, structured annotations into diverse instruction-folloiing data. Quality was ensured by carefully engincering and pilot-testing our prompts confirm that the LLM's output consistently and accurately reflected the factual input from our pipeline. The LLM was not used to generate novel clinical insights. Additionally, for evaluating the conversational task, we used GPT-5 as an impartial judge to score responses against predefined criteria.
Writing and Coding Assistance. LLMs were used to improve the clarity and grammar of the manuscript and to assist with routine coding tasks. We directed these processes and thoroughly reviewed, edited, and validated all outputs to ensure they align with our research goals and standards.
MORE RESULTS N
This section presents the responses of CerebraGloss-3H to three categories of instruction-follooinn questions in the CerebraGloss-Bench along with the human-revised answer keys from the bench- mark. A total of five examples are shown, covering four major categories of questions: artifacts, sleep, epilepsy, and background. CerebraGloss demonstrates its strong ability to understand vari- ous clinical waveform events, as well as the chamnels and time of their occurrence. However, false positives (model hallucinations) are frequently present, which we believe are primarily due to the un- avoidable inclusion many false positives in the automatically generated fine- seee-grrined EEG quality instruction- folllowinn data. We use strikethrough to mark the false positive parts.
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Severe artifact
35
F72-VV
F3-V
F4-V
C3-V
P3-VV
01-AV
02-AV
F74V
FR-VV
T4-VV
T5-AV
T6-AV
FZ-VV
P2.VV
Time (6)
Source: CerebraGloss--ee hwngnnch//////////////////////// 1Lav. jpg
User CerebraGloss
Summarize the main features this EEG.
The channel layout uses the standard 10-20 system with an average reference. The data quality is fair, with significant artifact interference. Extreme values are observed in Ol throughout the recording, and in FZ and CZ during 2.9-4.2 seconds, indicating artifact. High-frequency noise is persistently present in bilateral frontal, temporal, and occipital lobes, and in the parietal lobe midline. Frontopolar leads demonstrate cyeblink artifuets during 5:0 seeends Sporadic sharp waves are present in tight hemisphere tigtt--hghhh and frnntal eentrentral lobes mieline during mielitee-durinng 0.8 1.4 secends, and the right hemisphere and frontal and central lobes midline during 1.7-2.2 seconds, aidh-ndditional sharp waves in in-F4 and and-F7.ud duriingg 2.3-2.8 seconds. The background activity is stable, predominantly composed of medium-amplitude delta band activity at approximately 2.1 Hz with an amplitude of about 27.8 yV.
Answer Key
The channel layout the standard 10-20 system with average reference. The data quality fair, with significant artifact interference. Persistent high-frequency noise fects the bilateral frontal and temporal regions, and prominent electrode artifact is noted in the 01 and CZ channels. A generalized burst of sporadic sharp waves is observed from approximately 1.6 to 2.1 seconds. This is followed by medium-ann -mmplitude delta activity approximately 1.4 Hz, maximal from 4.0 to 8.0 seconds.
User
What best deseribes 01 and CZ?
A) sustained run delta activity.
B) Generalized, synchronous sharp waves.
C) Severe electrode artifact.
D) A well-organized posterior dominant rhythm.
CerebraGloss
#*C. ** Ol shows extreme values and CZ has sharp waves and spikes, which are charac-
teristic severe artifact technical issues. making them unreliable for interpretation. ++C.
Answer Key
User CerebraGloss
note
CZ differ from others. Are they epileptiform discharges?
While sharp waves and spikes are detected in 01 and CZ, the data quality in these chan- nels is compromised. There's 's period of extreme values in O1, and CZ shows significant artifact. Given this, these isolated sharp waves and spikes highly suspicious for arti- fact, especially the "spikes" CZ, which are likely part the artifact given the extreme values.
Answer Key
Certainly not. They represent severe electrode artifacts, likely caused by poor contact.
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Eye-related artifact and high frequency noise
FPI-VV 3E.0
FP2-V
F3-AV
F4-VV
C3-VV
C4.VV
P3-VV
P4AV
01-AV
F7-AV
T3-VV
T4-VV
T54V
T6-V
C2-AV
8
(8)
Source: CerebraGl ooseaeGloss--eeee BeeechBeccheeee htch///mmmmmmg 1mgg _6.... jpg
User CerebraGloss
What does this EEG show?
The channel layout uses the standard 10-20 system, using average reference. Data quality is fair with some artifact interference. Low-voltace channels are observed in CZ during 0.1 0.1-3.4s. Low-voltage channels are observed in CZ, PZ during 3.4-8.4s. High-frequene noise is persistently observed in bilateral frontal, temporal, central, and occipital lobes throughout the recording period. figg-ffreqeecc noise is observed in FP2 during 0.0- 0.7s, 2.4-3.4s. Frontopolar leads (FP1, FP2) demonstrate eyeblink artifacts during 0.6- 1.1s, 2.8-333,, 3.6-4.15 1s. 6.3-6.8s, 8.3-8.9s, 9.2-9.7s. Consequently, artifact-induced sharp waves are observed in bilateral temporal, central, parietal, and occipital lobes and central and parietal lobes midline during 0.5-1 22s, 3.5-4.1s, 8.3-8.9s, 9.2-9.7s. Right-gaze gight-gaze arti- facts detected in temporal leads (F7, F8) during 8.2-8.4s. Speradie sharps are peee-preeenteee FZ during 2.9-3.4s:
Answer Key
The channel layout quality is fair, though the recording significantly affected by various artifacts. Frequent eye blink artifacts are present, which temporally correlated with the delta activity in the bilateral frontal and occipital regions. Persistent high-frequency muscle artifact noted, predominantly
the standard 10-20 system with average reference. The data
the temporal areas. These findings consistent with relaxed wakefulness, with the recording significantly contaminated by myogenic and ular artifacts.
User
Regarding the waveforms resembling delta activity observed in F4, FZ, 01, and 02, which of the following descriptions is correct?
A) They are continuous sharp wave discharges.
B) They artifacts generated by eye blinks.
C) They benign vertex sharp waves characteristic of NREM sleep.
D) They are generated by structural brain damage.
CerebraGloss
BBB ** The description explicitly states that the delta activity in F4, FZ, 01, and 02 is cutifact-induced and co co-occurs with eyeblink artifacts in the frontopolar leads.
Answer Key
**B ** The delta activity occurs simultaneously with eye movements seen in the fron- topolar leads.
User
am noting the presence of delta activity in the frontal and occipital regions. Could these be associated with epilepsy, or are they concerning?
CerebraGloss
The delta activity noted in FP1, FP2, and FZ is primarily due to eye blink artifacts, par- ticularly the ones around 6.5-7.1 seconds and 8.4-9.0 seconds. These eye movements can sometimes induce artifactual slow waves in adjacent channels, which could be mis- interpreted as true cerebral activity. There are no clear epileptiform discharges or other significant abnormalities in this segment.
Answer Key
That's 's important point to clarify. The delta activity appear to be artifactual. They occur simultancously with the prominent eye blinks seen in the frontal channels. This is common finding where blink artifacts can be misinterpreted as sharp waves delta activity, especially in average reference montage.
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Sleep spindle
FP1-V
FF2-V
F3-VV
F4-VV
C3:VV
C4-VV
P4-V
02-AV
F7AV
FR-VV
T3-V
TS-V
T6-V
FZ-VV
CZ-VV
8 10
Source: Cerebra61oss- /img/ Lav. jpg
User CerebraGloss
Generate description of this DEG.
The channel layout the standard 10-20 system with average reference. The data
quality generally good, though high-frequency noise persistently observed and intermittently in FP1. No definite epileptiform discharges or physiological sleep waves are observed. The background activity is stable, predominantly composed of low-amplitude Beta rhythm at approximately 15.4 Hz with amplitude of about 13.5 μV. This pattern is consistent with normal awake The channel layout uses standard 10-20 system with an average reference. The overall data quality is good, though the P7 channel shows persistently low voltage, likely due to technical issue. Widespread sleep spindles present from approximately 3.1-4.8 seconds and again from 5.4-7.1 seconds. The background activity stable and composed predominantly of low-mmplitude beta and delta activity. These findings are consistent with stage NREM2 sleep.
Answer Key
User
What the most prominent electrographic feature visible this segment?
A) Well-formed sleep spindles
B) A generalized K K-complex
C) Rhythmic epileptiform discharges
D) Well- eell-organized alpha rhythm
CerebraGloss
#4 A. ** The segment prominently displays sleep spindles, which are characteristic wave-
forms seen during non-REM sleep stages.
Answer Key
+* A. ** The segment shows clear, generalized bursts of activity in the 12-14 Hz range, which are characteristic of sleep spindles, hallmark of NREM stage sleep.
User CerebraGloss
What your initial thoughts this segment?
This segment appears be largely unremarkable, showing stable background rhythm, While there's some high frequency noise, particularly in frontal and temporal regions, doesn't 't obscure the overall tracing significantly.
Answer Key
This appears to be a good quality recording of patient in sleep. The most prominent features two well-formed, generalized bursts of sleep spindles, which are character- istic NREM Stage sleep.
35

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Published as a conference paper at ICLR 2026
Epileptiform discharge
26
F72-V
F3-VV
F4.V
C3-V
P3-VV
01-AV
02-VV
F7AV
FR-VV
T4-VV
T5-AV
T6-AV
F2-VV
P2.VV
Time (6)
Source: CerebraGl wawaaGlloss-nenccc mmmg/////// 12.aa..p...
User CerebraGloss
What is happening in this EEG?
The channel layout uses the standard 10-20 system, using average reference. Overall data quality is good. Sporadic sharps and spikes are present in bilateral frontal, temporal, and central lobes during 0. 1-0.ss, 0.7-1.1s, 2.0-2.4s. Sporadic sharps present in FP1, FP2, F8 during 7.3-7.9s. Sporadic sharps are present T5 during 8.6-9.0s. Delta activity is observed in F7, T3, T4 throughout the recording period, with froquency approximately (37.2 The background activity is stable, predomi- + a-de-iannm-mddinntt medium ( nantly composed of medium amplitude Delta band (at approximately 2.1 Hz, amplitude about 25.8 vV).. Interhemispheric asymmetry observed in the background activity, man- ifested as: on F7/F8, T3/T4 leads, the amplitude on the side is consistently higher than that on the right side.
medium-anplitude
Answer Key
The channel layout uses the standard 10-20 system with average reference. The overall data quality is good. Sporadic independent spikes region (F7. T3, T5) at
the left anterior temporal 2s; and the right temporal region (F8, T4, T6) at 8.7s.
User
What the most accurate description of the epileptiform activity seen in this segment? A) single, well-defined left temporal seizure focus.
B) Generalized spike-and-waad and-wave discharges.
C) Focal, independent epileptiform discharges.
D) Benign Rolandic spikes.
CerebraGloss
**C.** The segment shows sporadic spikes and sharps, some localized the left tempo-
ral region (T3, T5), and others more widespread, indicating focal, independent epilepti- form activity rather than a single generalized or focal seizure.
Answer Key
+*C. The EEG shows distinct sharp waves and spikes occurring independently in both the left (F7/T3/T5) and right (F8/T4) temporal regions, which is the key finding.
User CerebraGloss
Can you elaborate on the epileptiform discharges in this segment?
Certainly. There periodic sharp waves and spikes observed bilaterally in the frontal, temporal, central, and parietal regions, occurring at approximately 0.6 Hz. Additionally, sporadic sharp waves are noted in the right frontal and temporal lobes, specifically around 7.4-7.9 seconds and 8.3-8.7 seconds.
Answer Key
Certainly. Early in the recording, there clusters of spikes localized the left temporal region at around Os, 1s and 2s. Then, much later the segment, separate and distinct group spikes appears over the right temporal area at around 8.7s. Their separation in time and distinct locations suggest two independent foci of irritability.
36

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Published as a conference paper at ICLR 2026
Delta activity
FP1-VV
P22-Y
F4-VV
C3-VV
C4.VV
P1-VV
01-AV
F7-VV
F8-AV
T4-VV
T6-V
FZ-AV
CZ-AV
PZ-AV
Time (6)
Source: CerebraGloss-Bencth (onng/885 ipg
User CerebraGloss
Provide a caption this EEG segment.
The channel layout uses the standard 10-20 system with an average reference. The over- all data quality is good. The recording is characterized by continuous, high-amplitude delta activity, approximately 1.3 Hz, observed diffusely across all brain regions. No def- inite epileptiform discharges or physiological sleep waves observed. This pattern is consistent with diffuse slowing hackground activity.
Answer Key
The channel layout uses the standard 10-20 system with an average reference. The overall data quality is good. The hackground activity is stable and predominantly composed of high-amplitude. diffuse delta activity approximately 1.8 Hz. If this is during sleep, could be the EEG pattern of Stage N3 sleep.
User
The most prominent feature of this EEG segment is:
A) High-amplitude, generalized delta activity.
B) A focal epileptiform discharge the right central region.
C) A normal, well-organn orgamized posterior dominant alpha rhythm.
D) Intermittent, rhythmic theta bursts.
CerebraGloss
** A. The segment shows widespread, high-amplitude delta activity across all channels, indicating generalized slowing of background activity.
Answer Key
A.
User CerebraGloss
Can you give me an initial impression of this 10-second segment?
This segment appears to be within normal limits, showing a stable hackground dominated by delta activity. There are no clear epileptiform discharges significant artifacts that would suggest pathology.
Answer Key
Certainly. The most striking feature of this segment is background activity. It quite slow, dominated by continuous, high-amplitude delta waves at approximately 1.8 Hz. The overall data quality appears good, which allows for clear analysis.
37


Reviews:
[{"review_id": "acBTkR0tDM", "reviewer": "Reviewer_8gRC", "summary": "This paper presents CerebraGloss, the first Large Vision-Language Model (LVLM)  fine-tuned for clinical EEG interpretation through instruction tuning. It transforms the traditional EEG interpretation task from a closed classification problem into a generative, multi-turn conversational explanation task, supported by an automated EEG–text instruction data generation pipeline and a new evaluation benchmark called CerebraGloss-Bench. The model demonstrates strong performance on standard datasets (TUSZ, HMC) and a custom benchmark, showing promising potential for both research and practical applications.", "questions": "Evaluation lacks objectivity and completeness: The dialogue evaluation relies on GPT-5 scoring, which may introduce subjectivity and bias. No human expert evaluation is provided, making it hard to assess the true clinical relevance of the generated content. In addition, CerebraGloss-Bench contains only 90 EEG segments, which limits the statistical significance of the results.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 4, "contribution": 4, "strengths": "CerebraGloss represents an important step toward generative and multi-task EEG interpretation empowered by LVLMs", "weaknesses": "Data quality and validation are insufficient: The impact of noise in the automated data pipeline is not quantitatively analyzed. The paper lacks comparison with models trained on manually annotated data, making it unclear how noise affects performance. The representativeness of the data distribution is also not well explained—although the training data come from several public datasets, it is uncertain whether they cover different ages and pathological EEG types. The model’s generalization ability on heterogeneous data remains questionable.\n\n\nMethodological depth could be improved: Converting EEG signals into images may lose important phase and amplitude dynamics, reducing diagnostic fidelity. The instruction tuning strategy heavily depends on an existing framework (Qwen2.5-VL), with limited architectural innovation introduced by the paper itself.\n\n\nWriting style and tone are somewhat promotional: Some phrases sound overly promotional, such as “we pioneer” and “significantly outperforms GPT-5”. There are also minor spelling errors (e.g., “Descripition” should be “Description”).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YjEeOkL1wR", "reviewer": "Reviewer_x7qx", "summary": "This paper introduces CerebraGloss, an instruction-tuned Large Vision-Language Model (LVLM) for nuanced interpretation of clinical EEG waveforms. The authors propose a full data-engine pipeline that generates synthetic EEG-text instruction data. The paper also introduces CerebraGloss-Bench, a benchmark of 90 EEG segments annotated for four evaluation formats (description, QA, MCQ, waveform detection). Experiments show improvements over general LVLMs (e.g., GPT-5, Qwen2.5-VL-3B) and new state-of-the-art results on TUSZ seizure detection.", "questions": "1. More analysis on the generated data can be conducted. For example, the quantitative analysis of annotation accuracy or comparison to human expert labels.\n\n2. Some stronger specialized baselines can be included.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. EEG interpretation is a clinically valuable yet underexplored problem in multimodal AI. The authors frame the shift from narrow classification to generative interpretation.\n\n2. The automated data engine is an engineering contribution. The combination of YOLO-based detection, spectral feature extraction, and rule-based/LLM caption synthesis is practical for a data-scarce field.", "weaknesses": "1. Although the pipeline is innovative, the core dataset is automatically generated with only light manual supervision. This raises concerns about noise propagation and factual validity in fine-grained annotations.\n\n2. The authors suggest “occasional hallucinations” in generated interpretations in ethics statement, but more quantitative analysis of annotation accuracy or comparison to human expert labels would increase confidence.\n\n3. CerebraGloss-Bench contains only 90 test segments.\n\n4. Comparisons are restricted to general-purpose LVLMs or LLaVA-Med which is relatively out-dated. Some more recent and stronger specialized LVLMs should be included for comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pDfY3WV2Qd", "reviewer": "Reviewer_NtLM", "summary": "The authors introduce CerebraGloss, a Large Vision-Language Model (LVLM) that is instruction-tuned for the interpretation of clinical EEG. The authors' core claim is that this model moves beyond the field's current focus on narrow classification tasks (like seizure detection) to enable unified, generative analysis. This allows the model to generate detailed waveform descriptions and engage in multi-turn, context-aware dialogue about an EEG segment.\n\nTo overcome the lack of suitable training data, the authors developed a novel, automated data generation pipeline to create a large-scale corpus of EEG-text instruction data. To evaluate the model's new capabilities, they also introduce and release CerebraGloss-Bench, a benchmark for open-ended EEG interpretation. The authors demonstrate that CerebraGloss outperforms 'off-the-shelf' LVLMs (including proprietary models) on this new benchmark and also performs strong classification on the TUSZ seizure detection classification task.", "questions": "- Why was the classification evaluation limited to TUSZ and HMC? Could the model's performance be reported on other standard tasks from the datasets you already used for data generation?\n- Why do the authors not provide any comparisons with Gijsen & Ritter, 2025 (given that EEG-CLIP is technically 'concurrent work')? It seems the most relevant work to yours.\n- Could you elaborate on the decision to limit CerebraGloss-Bench to 90 samples?  Given the small size, how confident are you in the benchmark's ability to robustly differentiate model performance?\n- Do the authors have any future intentions to release any of the data annotations or to expand the benchmark?\n- I'm wondering about the trade-off of presenting the EEG as an image versus operating in 'signal/timeseries' space. I presume it is necessary to move to images to be able to leverage the pretrained VLMs, but have the authors considered integrating a timeseries encoder with an LLM? I would not expect any analyses on this but it seems an important design decision about which it would be interesting to know the authors' reasoning.\n- In the Stage 1 ablation study (Section 6.3), you find the optimal checkpoint is at just 0.05 epochs to avoid overfitting and \"overwriting its powerful, pre-existing reasoning capabilities.\" This suggests the model starts to overfit almost immediately. Could you provide more intuition on why this overfitting on the \"template-heavy captions\" happens so rapidly, before even a single pass over the data? Is this visible in the model outputs?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Novel and valuable contribution: The paper aims to start a broader shift around machine learning for EEG. Moving from isolated classification to a unified, generative, and conversational model for EEG interpretation is a novel and interesting direction for the field - certainly worthy of exploration.\n- Significant data generation effort: The development of the automated data generation pipeline, especially the bespoke detector for waveform events, represents a substantial engineering effort. This pipeline is a key contribution to addressing the primary bottleneck (lack of fine-grained, large-scale paired data) that has held back this line of research.\n- New generative bnenchmark: The release of their benchmark is a welcome contribution. The field so far I believe entirely lacks benchmarks for evaluating generative and conversational abilities, and this dataset (though small) provides a much-needed tool for measuring progress in this new research direction.", "weaknesses": "- Limited classification evaluation: The paper introduces CerebraGloss as a model capable of both classification and generation. However, the classification evaluation seems limited. Given that the data generation pipeline sourced data from numerous public datasets (TUAB, TUEV, TUAR, etc.), it is surprising that the model's classification performance was not also benchmarked on tasks from these datasets (e.g., abnormality detection on TUAB or a more complex event classification from TUEV). This would have provided a much more comprehensive picture of its capabilities as a \"unified\" model.\n- Missing comparisons: The related work section discusses prior work like Gijsen & Ritter (2025) but dismisses it, stating it \"struggles to ground textual descriptions.\" It is unclear if this claim is based on a direct comparison or is a hypothesis. This work (together with EEG-CLIP (https://doi.org/10.3389/frobt.2025.1625731) although that one is 'concurrent' so comparisons cannot be requested, yet citation seems appropriate), seems like an important baseline. It is unclear why other existing models, which also bridge EEG and language, were not further discussed or included in the comparisons.\n- Small scale of the new benchmark: While CerebraGloss-Bench is a really valuable type of contribution, its scale of only 90 segments seems small. This raises concerns about the statistical robustness of the evaluation results presented in Table 3 and how well they might generalize.\n\nI would be happy to increase my score if the evaluations are extended to better support the claims about the classification ability of the model or if the paper's presentation is appropriately adapted to focus on the other strengths of the authors' work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QAyXGhLjHs", "reviewer": "Reviewer_id5r", "summary": "This paper introduces CerebraGloss, a large vision-language model (LVLM) instruction-tuned for clinical EEG interpretation. The key innovation is treating EEG waveforms as visual data and adapting LVLMs to interpret them through natural language. The authors develop an automated data generation pipeline featuring CerebraGloss-YOLO for waveform detection, create a large-scale instruction dataset (94K examples), and establish CerebraGloss-Bench as the first benchmark for open-ended EEG interpretation. The model achieves state-of-the-art performance on seizure detection (TUSZ) but not sleep staging (HMC) and shows convincing results on their novel benchmark (but no comparisons are available).", "questions": "- Validation: Have you conducted any pilot studies with neurologists comparing CerebraGloss interpretations to expert readings? What is the inter-rater agreement between your automated annotations and expert clinicians on a held-out validation set?\n- Have you conducted systematic error analysis on CerebraGloss-YOLO's outputs? What are the most common failure modes?\n- Could you provide confusion matrices showing which waveform types are most frequently confused?\n- Regarding potential hallucinations: Would incorporating uncertainty quantification improve clinical safety?\n- Have you tested extending context beyond 10 seconds using sliding windows or hierarchical processing?\n- Regarding ablation: Could you test the model without the Gemini-generated data to isolate the contribution of automated vs. LLM-augmented data?\n- Why only 90 segments in CerebraGloss-Bench? Is this sufficient for robust evaluation?\n- Further comparisons: Could you compare against multimodal medical models like Med-PaLM or BiomedGPT? What about domain-specific EEG models that don't use vision-language approaches?\n- Can you elaborate a bit more on what specific challenges prevent direct time-series encoding and why you are in the favor of treating EEG data as images?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Shifts from narrow classification to comprehensive, interpretative analysis of EEG, mimicking expert clinical practice\n- Novel approach in treating EEG data as images\n- CerebraGloss-Bench fills a critical gap with 90 expert-validated segments covering diverse clinical phenomena \n- New SOTA results on seizure detection (but not sleep staging)\n- Includes ablation studies on training stages, data composition, and model scaling\n- Open-source commitment: Plans to release model, benchmark, and tools to advance the field\n- Nice additional supplementary material for clinical context, more clinical examples and useful information on used prompts", "weaknesses": "- The practical interpretability of “neuro-glosses” remains somewhat subjective, and quantitative evaluation of explanation quality is limited.\n- Primarily applies existing LVLM techniques (Qwen2.5-VL) to a new domain without substantial architectural innovations\n- Treats EEG as images rather than native time-series signals (a comparison with methods operating directly on the time-series data would be helpful)\n-  CerebraGloss-Bench might be too small with 90 expert-validated segments (for robust validation)\n- Heavy reliance on automated pipeline introduces noise; authors acknowledge hallucination issues from false positives (also pointed out as limitation by the authors)\n- Limited set of comparisons (regarding multimodal or EEG models)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "II4h9sMhAx", "reviewer": "Reviewer_fjdA", "summary": "CerebraGloss introduces an instruction-tuned large vision-language model (LVLM) for fine-grained clinical EEG interpretation. The paper contributes (i) a programmatic data engine that converts raw EEG into structured annotations via a bespoke YOLO-based waveform detector (CerebraGloss-YOLO) plus background/artifact analyzers; (ii) a two-stage post-training pipeline on top of Qwen2.5-VL (3B/7B) that first aligns EEG visual concepts and then performs instruction tuning for generative and conversational tasks; and (iii) CerebraGloss-Bench, a 90-segment, expert-verified benchmark assessing descriptions, complex MCQs, QA, and dense channel-wise waveform detection. Empirically, CerebraGloss surpasses general LVLMs (including GPT-5) on the new benchmark (e.g., MCQ 80.0%, ROUGE-1 44.19, QA 4.76/10 judged by GPT-5) and achieves new SOTA balanced accuracy on TUSZ seizure detection, while remaining competitive on HMC sleep staging. Ablations support the two-stage design (notably very short Stage-1 alignment) and suggest scaling benefits at 7B.", "questions": "1. Can the authors report per-class precision/recall (or error taxonomies) for **CerebraGloss-YOLO** against expert labels on a held-out set, and estimate the noise rate in the generated instruction data? This would contextualize hallucination rates. \n2. Beyond GPT-5 judging, did clinicians perform blinded scoring (with inter-rater κ) on a subset of QA/description outputs? If not, could the authors add a small human study in the rebuttal? \n5.  Can the model surface **calibrated uncertainty**, highlight **low-confidence regions**, or defer to human review automatically? This is especially important given acknowledged hallucinations and ethics.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "*  Reframes EEG analysis from narrow classification to unified generative interpretation with multi-turn dialogue; introduces a YOLO-based waveform detector tailored to channel-wise EEG graphoelements; offers a practical two-stage alignment/tuning recipe that is efficient (≈4h per stage) and scales to 7B. \n*  Solid multi-facet evaluation: (i) CerebraGloss-Bench with free-text, MCQ, QA, and detection; (ii) standard clinical tasks (TUSZ/HMC); (iii) ablations on Stage-1 duration, Stage-2 composition, and model size. Clear reporting of balanced accuracy for imbalanced tasks and $mAP@0.5$ for detection. \n*  Pipeline is clearly described with concrete examples (e.g., montage, artifacts, spindles, K-complexes), plus a concise clinical EEG primer that defines background rhythms and graphoelements. Limitations and ethics are explicitly discussed. \n* The data engine and benchmark could catalyze research on general-purpose neuro-intelligent system*; reported SOTA on TUSZ indicates that the generative framing and instruction tuning can still deliver strong discriminative performance. Planned open-sourcing increases community value.", "weaknesses": "While the system is competently engineered, it largely follows a conventional recipe—fine-tuning a general-purpose foundation model on a domain-specific corpus with task-aligned instructions—without introducing a clearly new algorithmic idea. The components (instruction data curation, adapter/LoRA-style tuning, and standard loss/training schedules) appear incremental, and the paper does not articulate a principle or mechanism that would generalize beyond this application. As a result, the contribution feels more like a careful system instantiation than an advance in learning algorithms.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)