Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
Authors: Chenda Duan
Yipeng Zhang
Sotaro Kanai
Yuanyi Ding
Atsuro Daida
Pengyue Yu
Tiancheng Zheng
Naoto Kuroda
Shaun A. Hussain
Eishi Asano
Hiroki Nariai
vwani Roychowdhury
Venue: ICLR 2026 Poster (2026)
Keywords: Computational neuroscience
iEEG
Epilepsy
Computer Aided Diagnosis
Neurophysiology
Primary area: datasets and benchmarks
TL;DR: Omni-iEEG is a large, expert-validated iEEG dataset and benchmarks to bridge machine learning and epilepsy research.

Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent in format and metadata, lack standardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. With extensive efforts to reconcile heterogeneous iEEG formats, metadata, and recordings across publicly available sources,
we present $\textbf{Omni-iEEG}$, a large-scale, pre-surgical iEEG resource comprising $\textbf{302 patients}$ and $\textbf{178 hours}$ of high-resolution recordings. The dataset includes harmonized clinical metadata such as seizure onset zones, resections, and surgical outcomes, all validated by board-certified epileptologists. In addition, Omni-iEEG provides over 36K expert-validated annotations of pathological events, enabling robust biomarker studies. Omni-iEEG serves as a bridge between machine learning and epilepsy research. It defines clinically meaningful tasks with unified evaluation metrics grounded in clinical priors, enabling systematic evaluation of models in clinically relevant settings. Beyond benchmarking, we demonstrate the potential of end-to-end modeling on long iEEG segments and highlight the transferability of representations pretrained on non-neurophysiological domains. Together, these contributions establish Omni-iEEG as a foundation for reproducible, generalizable, and clinically translatable epilepsy research. The project page with dataset and code links is available at $\url{https://omni-ieeg.github.io/omni-ieeg/}$.

Published as a conference paper at ICLR 2026
OMNI-IEEG: A LARGE-SCALE, COMPREHENSIVE IEEG DATASET AND BENCHMARK FOR EPILEPSY RE- SEARCH
Chenda Duan
Yipeng Zhang**
Sotaro Kanai² Naoto Kuroda
Yuanyi Ding'
Atsuro Daida*
Pengyue Yu-
Tiancheng Zheng!
Shaun A. Hussain
Eishi Asano
Hiroki Nariai²
Vwani Roychowdhury
UCLA Samueli School of Engineering
"UCL Mattel Children's 's Hospital, David Geffen School of Medicine Children's Hospital of Michigan, Wayne State University School of Medicine
ABSTRACT
Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ.) relies on intracranial EEG (iHEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent format and metadata, lack stan- dardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. With ex- tensive efforts to reconcile heterogeneous iEEG formats, metadata, and recordings across publicly available sources, we present Omni-iEEG, a large-scale, pre- surgical iEEG resource comprising 302 patients and 178 hours of high-resolution recordings. The dataset includes harmonized clinical metadata such as seizure onset zones, resections, and surgical outcomes, all validated by board-certified epileptologists. In addition, Omni-iEEG provides over 36K expert-validated an- notations of pathological events, enabling robust biomarker studies. Omni-iEEE serves as a bridge between machine learning and epilepsy research. It defines clinically meaningful tasks with unified evaluation metrics grounded in clinical priors, enabling systematic evaluation of models in clinically relevant settings. Beyond benchmarking, we demonstrate the potential of end-to-end modeling on long iEEG segments and highlight the transferability of representations pretrained on nannneoooppyyillliccal domains. Together, these contributions establish Omni- iEEG foundation for reproducible, generalizable, and clinically translatable epilepsy research. The project page with dataset and code links available at nttpss://mm www.oniieg...ieeeoeemeemeoeeieee github. comomni- ieeg/
INTRODUCTION
Epilepsy affects approximately 3.4 million people in the U.S. and nearly 50 million globally, mak- ing it one of the most common neurological disorders (for Disease Control & Prevention, 2017]] Organization 2023). About 30% of patients have drug-resistant epilepsy. where seizures cannot be controlled by medication (Kwan et al.] 2010). Most of these patients experience focal seizures originating in specific brain regions (Johst & Cascino 2018), and successful treatment relies on accurately identifying the epileptogenic zone (FZ),, the brain area crucial for seizure generation. There are two primary interventions aimed at disrupting or removing the EZ: (i) implantation of electrodes for targeted electrical stimulation and (ii) surgical resection of the affected brain tissue (Clinic 2024). However, both strategies carry significant risks, including cognitive deficits resulting from damage to functionally critical regions (e.g., eloquent cortex) (Helmstaedter & Elger 2013). Localization of the
"qqual contribution. Corresponding author: <ywani @ucl a.ddu>

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Omni-iEEG Dataset
302 Patients, 178 Hours Recording, 36177 Event Annotations all Clinically Validated
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Anstomatl
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Explorstory Tasks
Evaluation
Figure 1: Overview of the Omni-iEEG Dataset and Benchmark.
EZ is typically guided by combination of inpatient observation, neuroimaging, and intracranial EEG (iEEG), including identification seizure onset zones (SOZ) and interictal spikes. Yet. SOZ-based resections do not guarantee seizure freedom (Rosenow & Lüders 2001). and non-invasive tests, such as scalp EEG, MRI, PET, and MEG, often fail localize the EZ. with sufficient precision (Jayakar al. (016). The current clinical standard involves iEEG studies to identify both the pathological brain regions (i.e., the EZ) and the functional anatomical areas that must be preserved to minimize cognitive side effects. However, this process relies heavily manual review of extended iEEG recordings, which is time-consuming and subject to low inter-rater reliability (Spring et al. 2017).
Several recent studies explore the use of machine learning iEEG data machine learning re- fined ncurophysiological biomarkers to facilitate epilepsy research; for example, network analysis (Partamian et al., 2025) and Convolutional Neural Network (Li et al. 2021b; Zhang et al. (022b). However, many of these efforts have been validated only on single-institution datasets with limited cohort sizes, restricting their clinical generalizability and robustness. Although datasets from in- stitutions such as (Fedele et al. 2017: Zhang et al., 2025a; Bernabei et al., 2023a Gunnarsdottir al. 2022) have been released, they differ in data formats and inconsistent channel naming and demographic metadata. Moreover, benchmark and evaluation metrics are not standardized across studies, limiting reproducibility and comparability. These inconsistencies hinder the ability derive translatable insights and to establish reliable benchmarks for model performance across studies.
To address these challenges, we construct Omni-iEEG, a large-scale, standardized dataset for epilepsy research. Omni-iEEG comprises recordings from 302 patients across 178 hours from eight leading epilepsy centers, including the University of California, Los Angeles; Wayne State University; the University Hospital Zurich; the University of Pennsylvania; the University of Miami; the National Institutes of Health; and Johns Hopkins Hospital. All recordings were obtained prior to surgical resection from patients with focal epilepsy, enabling models predict postsurgical outcomes from pre-operative data to simulate the surgical planning. Furthermore, since the iEEG recording comes with different formats and metadata, board-certified epileptologists (experts) verified and harmonized all recordings with consistent iEEG formats, channel annotations, and clinical metadata. All data were fully de-identified with institutional IRB approval public-domain release agreements.
Beyond the recordings and metadata, Omni-iEEG also releases clinically meaningful pathological biomarkers, extensively annotated by board-certified experts support robust biomarker research. We focus on one of the most promising clinically utilized iEEG biomarkers for localizing the epileptogenic zone: high-frequency oscillations (HFOs). HFOs have garnered growing interest in both clinical (Gotman 2010; Zweiphenning et al., 2022; Frauscher et al.) 2018b) and computa- tional (Kuroda et al., | Sciaraaffa et al.] 2020) Chaibi et al. [0144] Daida al.. 2025) domain. Despite their potential, the clinical utility of HFOs remains contested due to persistent challenges in distinguishing pathological from physiological HFOs Zijilman et al., 2012 Zweiphenning et al. 2022), as well as issues such artifact contamination and inter-rater variability (Nariai et al. [0088 Spring et al. 2017] ZZang et al. 2025b). Specifically, Omni-iEEG releases annotations of candidate HFOs, focusing the most widely accepted pathological definition: HFOs co-occurring with spikes machine-generated
(spkHFO). These annotations are conducted on machine- generated detections from multiple widely
used HFO detection algorithms (Navarrete et al. 2016 Ding al. 2025).
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To promote reproducible research and systematic model evaluation, define clinically meaningful benchmark tasks with corresponding evaluation metrics. The two primary tasks are: (1) Clinical Prior-Driven Pathological Event Classification, which focuses identifying iEEG HFO events consistent with expert-defined pathological patterns; (2) Pathooogcaa Brain Region Identification, which aims localize regions likely involved in seizure generation. In addition, propose set of exploratory tasks, including Anatomical Location Classification, Ictal Period Identification, and Sleep-Awake Classification that hold comparable clinical significance yet present greater analytical challenges for iEEG.
We rigorously build baseline models from both machine learning and computational neuroscience domains, leveraging learning from clinically validated biomarkers well as direct end-to- data- driven approaches. Omni-iEFG serves as the first comprehensive dataset bridging epilepsy research and machine learning, with the potential to accelerate clinically relevant discoveries and improve patient outcomes. Our contributions can summarized as follows:
We introduce the Omni-iEEG dataset, large-scale, standardized collection of interictal pre- surgical iEEG recordings from 302 patients and 178 hours of data, accompanied by rigorous clinical metadata verification, and includes over 36K expert annotations of pathological events.
We define comprehensive evaluation metrics grounded in clinical priors and compare baseline models from diverse disciplines, providing a unified benchmark that improves reproducibility, enables comparability across studies, and bridges clinical research with machine learning.
We demonstrate new insights enabled by Omni-iEEG, showing that end-to-end data-driven mod- cling of long iEEG segments can match or surpass clinically grounded biomarkers in epilepsy surgical planning, and reveal cross-domain transfer from pretrained audio representations to iEEG.
2 RELATED WORK
Public iEEG Datasets. Several public iEEG datasets, such as Open iEEG (Zhang et al., 2025a).
Zurich iEEG HFO (Fedele et al. 2017). HUP (Bernabei al., 2023a) and Epilepsy Interictal (Gun narsdottir et al., 2022), provide valuable resources for epilepsy research. However, these datasets suffer from inconsistencies in metadata annotation and formats, making them challenging for machine learning applications without clinical expertise. There are more open-sourced iEEG data available (Li (ct al. 2021a| Huang et al., 2024 Berezutskaya et al. 2022 Wang et al. 2024 Madan et al. 2024), but out of the scope of epilepsy research, or containing only ictal recordings. The Omni-iEEG dataset addresses these issues by offering harmonized data across multiple centers, improving accessibility and usability for both clinicians and researchers.
Clinically Discovered iEEG Biomarkers. HFOs are well-established biomarkers for localizing
epileptogenic zones, though distinguishing pathological from physiological HFOs remains chal-
lenging due to artifact contamination and inter-rater variability (Gotman] 2010; Frauscher et al.] 2018b Zijlmans al.) 2012 Nariai al. 2018). Despite these challenges, HFOs remain central to iase- epilepsy localization (Navarrete et al. 20166. Other interictal biomarkers also contribute localizing epileptogenic zones, including interictal spikes and sharp waves (Doose al. 1996),
infraslow activity (Rodin et al. 2014), interictal epileptiform discharges (de Curtis et al. 2012).
and chase-amplitude coupling (Samice al.) (0188;; however, these have seen limited adoption in recent clinical research. In the Omni-iEEG dataset, we focus on HFOs, as they are prevalent in existing literature and studies, enabling better integration of clinical findings with machine learning approaches for future research.
Machine Learning-Basse Data-Driven iEEG Biomarkers. Machine learning techniques, including deep learning models, have shown promise in classifying iEEG signals and identifying epileptogenic regions (Li al. 2021b Monsoor et al., 2023 Partamian et al. 2025 Sheikh et al. 2024 Wang et al. 2022). However, these models often rely small, single institlistttttionn datasets, limiting generalizability. The Omni-iEEG dataset, with its multi-center data, addresses these limitations and supports the development of more robust, clinically applicable models.
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3 OMNI-IEEG DATASET
The Omni-iEEG dataset aggregates recordings from multiple independently curated datasets, spanning diverse set of institutions and recording devices. The datasets vary in sampling rates, recording durations, modalities, and the availability of clinical metadata, such seizure onset zones, resection zones, and surgical outcomes. A summary of the dataset statistics is provided in Table 1
Table 1: Overview the Omni-iEEG dataset. "#Subjects" indicates the total number of subjects in the dataset; "#Selected" denotes those included in the current split; "#Surgery" refers to subjects who underwent resective surgery; "#Seizure- Freeee indicates subjects achieves post-operative seizue-free; "Duration" specifies the range of recording lengths in minutes.
Split
Dataset
#Subjects
#Selected
#Surgery
#Seizure- Free
Duration (Min)
OpeniEEG
111
111
97
66
5-111
Zurich
12
12
9
1-8
Training
HUP
42
9
5
SourceSink
21
12
1-16
OpeniEEG
74
74
67
48
5-120
Zurich
3-12
Testing
HUP
16
6
5
SourceSink
18
15
9
1-17
Total
302
255
215
155
Patient Cohort. Since current open-source iEEG datasets are independently collected and released by different institutions, we conduct comprchensive review of recently published epilepsy studies and assembled a unified collection of iEEG recordings to compose our Omni-iEEG dataset. Each dataset source is carefully referenefe with its corresponding publication ensure consistency between the reported data specification and the shared data. We select high-quality recordings from each source by prioritizing those with reliable metadata alignment, particularly clinical channel annotations. The patient cohort included in the Omni -EEG dataset is primarily drawn from the following data sources: Open iEEG Dataset (Zhang al., 2025a). Zurich iEEG HFO Dataset (Fedele et al. 2017), Epilepsy Interictal Dataset (Gunnarsdotttir al. 2022). HUP dataset (Bernabei et al. 2023a). After our rigorous cleaning and validation, we aggregate total of 302 patients and 178 hours of iEEG recordings. The detailed specification of the patient cohort is presented in the Appendix [
Data Processing and Harmonization. We adopt the standardized preprocessing protocols defined by each source dataset when constructing the Omni-iEEG collection (Fedele et al. 2017; Gunnarsdottin
et al. 2022 Zhang et al., 2025a Bernabei et al. 2023a). All recordings are reviewed by board-certified climicians ensure data quality. To address heterogeneity across datasets, clinicians harmonize metadata into a unified schema, enabling consistent use. These data harmonization decisions are grounded in established clinical conventions and expert judgment: steps that straightforward for clinicians but non-trivial for researchers without specialized training. All recordings in the released dataset are preserved at their original sampling rates as provided by the source datasets. For benchmarking and model training, we resample signals to 1000 Hz to standardize inputs; we provide per-recording sampling-rate metadata and a reference resampling script reproduce this step consistently in downstream pipelines. Further details of validation, discrepancy resolution, and metadata harmonization are provided in the Appendix[] and an ablation study on the sampling rate conducted in Appendix
Data Splitting. To facilitate standardized evaluation and ensure clinical relevance, we split the Omni-iEEG dataset into training and testing subsets using 60%/440% ratio at the subject level. We carefully consider key metadata attributes to guide the split. Specifically, we ensure that patients from each contributing dataset are evenly distributed across the training and testing sets. In addition, stratify the split to balance clinically meaningful variables, including resection outcomes, number of recording channels, and recording modalities to ensure that both subsets representative of the overall cohort. For specifcc evaluations, the training and testing sets may be further restricted to subsets of the full splits to retain the original split logic wherever feasible.
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Table Summary of HFO event annotations. "#Subjects" denotes the number of patients: "#Total Events" refers to all annotated HFO events; "#Artifact" indicates events labeled as artifacts; "#Real
HFO" represents real HPO; and "#spkHFO" refers to real HFOs that co-occur with spikes.
Split
Dataset
#Subjects
#Total Events
#Artifact
#Real HFO
#spkHFO
OpeniEEG
20
19909
5517
14392
10070
Zurich
829
242
587
449
Training
HUP
178
42
136
120
SourceSink
3
1120
949
171
121
OpeniEBG
14
13092
2052
11040
7964
Zurich
2
443
248
195
102
Testing
HUP
2
91
82
9
4
SourceSink
2
515
156
359
348
Total
49
36177
9288
26889
19180
4 BENCHMARK TASKS FOR INTERICTAL IEEG ANALYSIS
4.1 TASK 1: CLINICAL PRIOR-DRIVEN PATHIOLOGICAL EVENTS CLASSIFICATION
HFOs have been widely recognized as a key biomarker for localizing the epileptogenic zone in iEEG recordings. They spontaneous iEEG events in the 80-500 Hz frequency range, characterized by at least four consecutive oscillations that clearly stand out from the background activity. HFO events vary in duration, typically ranging from 30 to 100 milliseconds (Zelmann al.. 2012). However, refining the clinical utility of HFOs requires further filtering of detected events, as legacy detection algorithms often produce noisy outputs (Zweiphenning al. 2022). Due the lack of consensus on the precise definition of HFOs, we adopt consensus-drie driven approach by incorporating multiple mainstream detection algorithms (Navarrete et al. 2016) to create a comprehensive pool of candidate HFO events from multi-center iEEG recordings. Specifically, we utilize the Short- Time Energy detector (Staba et al. 2002), the Montreal Neurological Institute and Hospital detector (Zelmann) et al. 2010), and the Hilbert HFO detector (Crépon et al., 2010) to maximize detection coverage.
Four board-certified clinicians manually annotate each candidate HFO event as either an artifact (including ringing, muscle, and background fluctuations), pathological HFO, non-pathological HFO. We adopt the clinically promising definition of spkHFOs (Bénar et al. 2010), which are HFO events occurring in close temporal proximity interictal epileptiform spikes. Although there is no consensus on the computational definition of spkHFOs (Zhang et al. 2022b), we use expert annotations from multiple clinicians across more than 36K candidate HFOss to develop data-driven definition. Based on these annotations, the objective of this task is to perform multi-class classification predict whether given HFO event is spkHFO, non-spkHFO, of class imbalance across the three categories, we adopt macro-averaged precision, recall, Fl score, and area under the ROC curve (macro-AUC), The macro-AUC is computed one-vs-rest fashion and averaged equally across all classes. The classification dataset comprises 36,177 annotated HFO events, including 9, 9,288 artifacts, 7,709 non-spkHFOs, and 19,180 spkHFOs. A summary of the annotation distribution is provided in Table [ with additional details on the annotation protocol inter-rater agreement, and detector-specific statistics available in the Appendix
artifact. To mitigate the effect
4.2
TASK 2: PATHOLOGICAL BRAIN REGION IDENTIRICATION
4.2.1
MOTIVATION AND CLINICAL BACKGROUND
Accurate localization of pathological brain regions is critical for guiding effective epilepsy treat- ments, including surgical resection (Baumgartner et al., 2019) and neurostimulation (Johnson et al.) 2022). Current clinical workflows, relying on prolonged iEEG monitoring, cortical stimulation, ictal recordings, and interictal biomarkers, are time-consuming, resource-intensive, and require extended inpatient monitoring and close coordination among clinical teams (Jayakar et al.) 2016 Nariai et al.) 2018). While HFOs promising interictal markers of epileptogenicity. relying solely them may overlook other informative iEEG features (Gunnarsdottir et al., (002). A comprehensive data-driven approach that integrates spectral, temporal, and spatial characteristies has the potential to uncover
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Table 3: Summary of channel labels and clinical outcomes across dataset splits. "#Subjects" indicates the number of valid subjects; "#Normal/Patholggica Channels" represent iEEG channels labeled as normal or pathological: "#Seizure-Free/Non Free"" correspond to subjects with or without post- surgical seizure freedom; "#Anatomical Labeled" lists subjects with anatomical channel annotations.
# Valid Subjects
# Normal Channels
# Pathological Channels
Seizure
Seizure Non-Free
Split
Dataset
Free
Anatomical
OpeniEEG
110
4638
1072
66
32
111
Zurich
12
5619
9
3
Training
HUP
1034
106
3
SourceSink
500
177
OpeniEEG
74
3533
575
48
19
74
Zurich
2611
Testing
HUP
874
78
2
SourceSink
14
279
154
5
9
Total
252
19088
2162
154
79
185
novel biomarkers and improve robustness, interpretability. and generalizability. Recent work (Gun narsdottir et al. 2022 Wang et al., 2022 Chen et al. 2022) suggests that epileptogenic regions can be inferred from short interictal recordings. Although clinical decisions resection or stimulation must still integrate multiple factors (Tamilia et al.) 2018 automated analysis of interictal data offers path toward streamlining pre-surgical evaluation and enhancing treatment planning.
4.2.2 TASK SPECIFICATION AND EVALUATION METRICS
Clinical Evidence. Since the precise localization of pathological brain regions cannot be determined
prospectively (prior to surgery), the community often relies on established clinical evidence made retrospectively (after surgery) to guide the construction of this task (Zhang al. 2022b): : (1) SOZ channels. identified during monitoring, are expected to show interictal pathological activity; (2) Resected but non-SOZ channels are ambiguous, since margins depend on anatomical and surgical factors; (3) In seizure-free patients, preserved channels are presumed normal, as resection likely covered all pathological regions; (4) non-seizure-free patients, residual pathological regions are inferred, though their exact locations remain unknown.
Task Definition. The objective of this task is to develop a biomarker or model that classifies iEEG channels as either pathological or normal. We introduce two evaluation criteria for this task: (1) the model should distinguish between channels with high-confident labels: such as SOZ versus preserved channels in seizure-free patients; (2) it should provide patient-level predictions that are predictive of post-operative outcomes. Together, these evaluations aim to assess both the discriminative power of the model the channel level and its clinical relevance.
Evaluation at the Channel Level. The channel classification label is defined as follows: Channels marked SOZ during clinical monitoring are treated as positive examples, while preserved (non- resected) channels from seizure-free patients serve as negative examples. We report standard metrics including macro-averaged precision, recall (sensitivity), specificity (true negative rate), and the area under the ROC curve (AUC). Besides the AUC, given the clinical implications, we also place particular emphasis on both recall and specificity to minimize the risks associated with false negatives (missed pathological tissue) and false positives (unnecessary resection of healthy tissue).
Surgical Outcome Prediction. To evaluate whether the model's 's predictions correlate with surgical outcomes, following prior literature (Zhang et al.] 2022a b. 2025a Gunnarsdottir et al. 2022 Monsoor ct al. 2023), we compute a resection ratio (RR) for each patient. For every channel c, the model produces pathological score s., and RR is defined as: RR > Legreeeted 5e/ > 8c. This metric represents the proportion of predicted pathological signal that is surgically removed. We assign a binary outcome label each patient: for seizure-free free (Engel Class I) and 0 for not seizure- -free (Engel Class II-IV). We assess the predictive ability of the resection ratio using ROC-AUC, under the hypothesis that higher RR corresponds higher likelihood of post-operative seizure freedom.

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Cohort Summary. The pathological brain region identification task encompasses total of 252 patients and 21250 candidate channels, comprising 2162 pathological (SOZ) and 19088 normal channels. Within these patients, 233 patients underwent resection treatment, and 154 of them became seizure-free after resection. A comprehensive summary of this task is presented in Table 3
4.3 EXPLORATORY TASKS
Beyond the core benchmarks, we introduce three exploratory tasks that highlight clinically meaningful yet technically challenging problems in iEEG analysis. These tasks, though limited to data from one or two institutions, encourage new directions for data-driven epilepsy research. Full details and baseline results are in the Appendix C and
Anatomical Location Classification. This task aims to predict the anatomical location of iEEG signals using short segments. Accurate anatomical decoding can aid in functional mapping and surgical planning. Traditional methods rely on manual electrode labeling through stimulation, which time-consuming and prone variability. Automated classification improves precision and efficiency, overcoming these limitations (Bernabei et al.) 2023b).
Ictal Period Identification. This task distinguishes between interictal and ictal periods in iEEG segments, which is crucial for localizing seizure onset zones and evaluating interventions. Manual identification is
and prone missing brief scizures, whereas automated detection
improves diagnostic accuracy and timeliness (Fisher et al. 2014 Alhilani et al. 2020).
Sleep-Awake Classification. This task classifies iEEG segments into sleep awake states, seizure occurrence and interictal discharges are influenced by the sleep-wake cycle. Automated classification enhances diagnostic accuracy and treatment planning by overcoming the subjectivity and oversight inherent in manual classification (Derry & Duncan 2013).
BENCHMARK RESULTS
5.1 PATHOLOGICAL EVENTS CLASSIFICATION
Baseline Methods. HFO events are eigh-frequency ID signals with complex temporal and spectral dy- namics. Previous interdisciplinary studies in HFO analysis (Zhang et al. 20224) have primarily focused on the morphology of iEEG traces, often using time-frequenyy representations (Morlet Wavelet). Building this work (Zhang et al., 2 we retrain their model on Omni-iEFG annotations, PyHFO-Omni. Additionally, we evaluate raw iEEG traces as a time-domain representation of HFO events, implementing baselines inspired by recent time-series models, including an LStM-Attention (inspired by (Huang et al.) 2023)). Transformer based on the PatchTST architecture (Nie et al. 2023). We also train the competitive time-series framework, TimesNet (Wu et al.) 2023a) on HFO event classification. Implementation details of each baseline model are provided in the Appendix E
Performance Comparison. As shown in Table 4 the climically validated framework, PyHFO-Omin achieves the highest Fl score and recall, making it a strong baseline. However, time-domain baselines exhibit suboptimal performance. We hypothesize that since the spike (sharp waveform) and HFO characteristics lie within the high-frequency band, the subtle dynamics of the iEEG trace may not be fully captured by directly consuming the raw iEEG data. Additionally, time-domain basclines may pose difficulty in accounting for the correlations between different frequency bands, further complicating the learning task.
Table 4: Benchmark for Pathological Event Classification. Evaluation metrics include macro-averaged precision, recall, FI and AUC.
Model
Precision
Recall
F1
AUC
Attentinn
0.7352
0.7359
0.7338
0,9109
PatchTST Transformer
0.7757
0.7686
0.7726
0.9311
TimesNet
0.7589
0.7726
0.7652
0.9221
PyHFO-Omni
0.8025
0.8110
0.8061
0.9390
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5.2 PATHOLOGICAL BRAIN REGION IDENTIFICATION
To identify pathological brain regions, we evaluate two classes of baseline models: (1) approaches based on clinically validated biomarkers, HFO events (event-base based models), and (2) purely data-driven approaches that directly learn from the iEEG segments (segment-basee models).
Event-Based Models. Motivated by the clinical hypothesis that pathological regions exhibit higher rates of pathological HFOs, we first detect candidate HFO events (see 4.1 on each channel. To identify pathological HFOs, we use three models: (1) an eHFO classifier (Monsoor et al.) 2023).
which defines pathological HFOs using weakly supervised learning with clinical evidence, and (2) PyHFO-based classifiers, including the built-in model (Zhang et al. 2024) and our extended PyHFO- Omni (Sec.5..1 both used to predict spkHFOs as pathological HFOs. After generating predictions, we compute the per-channel pathological HFO rate by counting pathological events. To derive binary channel-level predictions, we compute the ROC curve and use Youden's statistic to select the optimal threshold. With this threshold, we obtain binary predictions and report macro-averaged precision, recall, specificity, and Fl score. For outcome prediction, we compute the resection ratio (RR) using the raw pathological rates and evaluate AUC, as defined in Sec 4.2.2
Segment-Based Models. These models operate directly on raw iEEG segments without relying on intermediate event detection. During the training, we adopted stratified sample to uniformly select the iEEG segments from each class. The segment duration is set to one minute, providing a broader spectrum than that of HFOs (normally 30-100ms) For evaluation, conduct inference on non-overlapping 1-minute segments from each channel in a sliding window manner to assign probability for each segment. Then compute a per-channel pathological score as the average of the pathological probability. Metrics reported in channel-level evaluations follow the same scheme as event-based models. Meanwhile, RR also calculated in the same manner the event-based model to standardize the outcome prediction.
use the models to
To establish an insightful and comprehensive benehmark for the segment- based model, we examine applications across diverse machine learning domains. We implement SEEG-NET (Wang et al. 2022), a model motivated by clinical knowledge for classifying pathological activity. Moreover, given the long duration of the 1-minute iEEG signals, which are one-dimensional in nature, we explore the fine-tuning of state-of of-the--rt audio processing frameworks Contrastive Language-Ain Audio Pretraining (CLAP) (Wu et al. 2023b), on iEEG data. Furthermore, inspired by event-based models, we implement a vision-style architecture, denoted as TimeConv- -NNN which processes 1-minute time- frequeeyyy plots of 1000 Hz iEEG signals. The model first applies ID convolution along the time axis to capture long-range temporal dynamics, followed by CNN layers for joint temporal-spectie feature extraction. Implementation details are provided in the Appendix F
Performance Comparison. Table 5 summarizes results for pathological channel classification and postoperative outcome prediction. The bveee-based model grounded in clinical priors (PyHFO-Omni) and the segment-based model TTimeonn-CNNY trained end-to-end achieve similar outcome AUCs (0.74 vs. 0.73). Crucially, TimeConv-CN not only matches outcome prediction but also outperforms PyHFO-Omni in pathological channel identification, achieving the highest channel-level AUC (0.81). By contrast, SEEG-NET attains strong classification AUC but low specificity, limiting its clinical utility. These findings highlight the need for multi multi- evaluation beyond outcome AUC, while also underscoring the potential of end-to-end approaches to transform iEEG-based epilepsy research.
5.3 BEYOND BENCHMARKING: TRANSLATIONAL INSIGHTS FROM OMNI-IEEG
Clinical Generalizability. Publicly available event-based models trained on single-center datasets perform poorly in our benchmark, underscoring the importance of nulti-institutioaal datasets like Omni-iEEG for building clinically reliable and generalizable models.
Direct Modeling of Minute-Long iEEG Segments. Direct end-to-end modeling of 1-minute intracranial EEG recordings at kilohertz resolution has not been fully explored in prior work. Existing approaches have largely focused on short, event-centered windows (e.g., HFOs spikes) or relied on heavily downsampled features, both of which discard long-range temporal dependencies. We address this challenge through two complementary strategies: (i) leveraging powerful representations from a different domain by treating iEEG as an audio signal, and (ii) designing a custom architecture, TimeConv-CNN tailored to the unique scale and resolution of iEEG.
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Table 5: Benchmark results for identifying pathological brain regions. Evaluation has dimensions: (1) Pathological Channel Identification, based on clinical priors of Pathological(SOZ channels) vs. normal (preserved channels in seizure-free patients); (2) Outcome AUC, which assesses whether higher scores in resected channels correlate with postoperative seizure freedom via the resection ratio.
Pathological Channel Classification
Outcome
Model
Precision
Recall
F1
Specificity
AUC
AUC
Event-Based Models
eHIFO
0,6053
0.6466
0.6195
0,4101
0.6611
0,4521
PyHFO
0.6000
0.6431
0.6140
0.4089
0.6557
0.4972
PyHFO-Omniąasoo
0.5799
0.6991
0,5635
0.6951
0.7351
0,7438
Segment- Models
SEEG-NET
0.5790
0.7169
0.5259
0.6049
0.7850
0.5952
CLAP
0.5936
0.6997
0.6009
0.7823
0.7684
0.6770
TimeConv-CNN
0.6259
0.7454
0.6469
0.8230
0.8061
0.7380
Cross-Domain Transfer with Audio-Pretrainel Models. Remarkably, CLAP, audio peatto-prerrineed
model optimized for generic acoustic signals rather than neurophysiology. achieves competitive performance on pathological channel classification after fine-tuning. This highlights the cross-domain generalization ability of audio representations to iEEG, where sequeeeeence dynamics can be repurposed as clinically meaningful features.
TimeConv-CNN for cies and fine- era-grained gegh-frequecc characteristics, we introduce TimeConv-CNT This architecture applies temporal convolutions directly to the large time-frequenc representation (60,000 time points x 224 frequency components), enabling efficient compression while preserving salient dynamics across scales. With efficient yet expressive design, TimeConv-Ch opens new pathway for identifying pathological brain regions, where both coarse and detailed patterns are critical.
Dynamics. To capture both long-range temporal dependen-
A Potentially "Hear-able" Biomarker. The competitive performance of fine-tuning audio- pretrained model like CLAP prompted a more direct question: If iEEG signals can be processed like audio, could their pathological features also be directly "heard"" To illustrate the potential for novel, cross-domain biomarker discovery, we conduct an exploratory case study on a representative eegUCLA01), patient (sub-open) (uab-oppnieegUCLALL) We convert one- min-mintte iEEG segments into audio waveforms and use an audio classifier (YAMNet) (Gemmeke et al. 2017) to label them. Specifically, each segment is normalized with z-scoring, time-compressed by factor of 10, resampled to 16 kHz, and noak-normllized to produce standardized audio waveforms. The results are striking: among 3,128 one-minute segments, including 276 from SOZ channels and 2,852 from preserved channels; the top-1 prediction, YAMNet labels 87 SOZ segments as "helicopter" while never assigning this label to preserved channels; expanding to the top-5 predictions, "helicopter" appears in 159 segments overall, compared to only 5 labeled as "physiological." While this N=1 observation may not be generalizable, serves as a compelling proof-of-concepl that clinically relevant neural dynamics may possess intuitive, "hearable" signatures. This opens a novel and highly interpretable direction for future biomarker discovery.
6 CONCLUSION AND LIMITATIONS
We present Omni-iEFG, large-scale, pre-surgical iEEG dataset comprising 302 patients and 178 hours of high-resolution recordings. This dataset is thoroughly verified with clinical metadata with released annotations of the pathological HFO biomarker. We define two primary and three exploratory clinically meaningful benchmarks, grounded in clinical insights established in the literature. We further benchmark multiple baselines across various disciplines, leveraging both clinically defined biomarkers and end-to-ent data-driven approaches for comprehensive evaluation. Beyond quantita- tive performance, our benchmarking also yields translational insights, highlighting opportunities for novel biomarkers and cross-domain modeling strategies with direct clinical relevance.

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Despite the significance and utility of the Omni-iEEG dataset and its associated benchmarks, several limitations remain. These include potential gaps in the clinical insights captured and the inherent subjectivity of spkHFO annotations, even when annotators reached consensus with high inter-rater agreement in this study. Additionally, while the benchmarks cover key tasks in epilepsy research, certain alternative HFO detection methods and artifact removal strategies
explored.
These limitations largely reflect the breadth of epilepsy research, where decades of diverse method- ologies cannot be encompassed in single study. Nonetheless, they underscore the importance of Omni-iEEG in providing a standardized foundation for comprehensive evaluation and platform for future exploration. We anticipate that our effort will not only support more reproducible bench- marking but also catalyze new methods, biomarkers, and clinical insights that advance both machine learning and epilepsy treatment.
Further details, including event annotation procedures, dataset harmonization and specifications, exploratory tasks, implementation details, interpretation analyses, ablation studies such as cross- dataset generalization, and an extended discussion of limitations, are provided in the Appendix.
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ETHICS STATEMENT
The Omni-iEEG dataset is principally constructed from four publicly available resources hosted on OpenNeuro: the Open iEEG Dataset, the Zurich iEEG HFO Dataset, the Epilepsy Interictal Dataset, and the HUP dataset. All data were fully de-identified with institutional IRB approval or public-domain release agreements. Each these datasets is released under CCO license, thereby permitting processing, integration, and redistribution, provided that the original sources are appropriately acknowledged.
REPRODUCIBILITY STATEMENT
The Omni-iEEG dataset has been reorganized in accordance with the Brain Imaging Data Structure (BIDS) standard and will made publicly available via Hugging Face. To support reproducibility and adoption, we will also release accompanying software library that provides utilities for streamlined data loading, flexible filtering using clinical metadata, and ready-to-use training and evaluation pipelines for benchmarking and model development. Furthermore, model checkpoints corresponding to all baseline methods will be made available to facilitate comparison and future research.
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APPENDIX
CONTENTS OF APPENDIX
The Use of Large Language Models
18
Pathological Events Annotation
18
Exploratory Tasks
19
Anatomical Location Identification
19
C.I.1
Motivation and Clinical Background
19
Task Definition and Evaluation Metries
19
C.2
letal Period Classification
20
C.2.1
Motivation and Clinical Background
20
Task Definition and Evaluation Metrics
20
3
Sleep Awake Classification
21
C.3,
Motivation and Clinical Background
21
Task Definition and Evaluation Metries
21
Exploratory Tasks Baseline Results
Implementation Details
22
E.1
Frequenyy Representation
22
Event-Based Models.
23
Segment-Based Models.
23
Training Hyperparameter
24
Compute Resources and Reproducibility,
24
Additional Experiments and Analyses
24
Ablation Study Cross- Generalization
24
Ablation Study on Channel-level Segment Length
26
Ablation Study on Sampling Rate
26
F4
Model interpretability Analysis
26
................................
Additional Baselines for Pathological Brain Region Identification
27
iiiitttins
27
Future Work
28
Detailed Dataset Specification and Harmonization
28
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A THE USE OF LARGE LANGUAGE MODELS
We used Large Language Models, such as ChatGPT, as general-purpose assist tools to polish the writing (grammar, phrasing, and readability) and aid in literature discovery by surfacing potentially relevant related work. All scientific ideas, experimental designs, analyses, and conclusions were developed by the authors, and LLMs did not contribute the level of scientific contributor or author. The authors take full responsibility for the entirety of the paper's 's content.
B PATHOLOGICAL EVENTS ANNOTATION
The annotation protocol used in the current study adopt consistent and standardized protocol from the prior studies (Narai et al. 2018) for event annotation. Regarding patient selection and detector choice, we introduce diversity ensure broad coverage across varying conditions and recording setups.
Subject Selection. Patients selected from diverse clinical scenarios in the Omni-iEEG dataset, including those undergoing different recording modalities and from multiple institutions. The overall annotations include iEEG recording from eight centers in Omni-iEEG dataset, with both ECoG and SEEG recordings represented. A summary of the patient and event distributions can be found in Table the main paper. Events detected by the Short-Term Energy (STE), Montreal Neurological algorithms were independently annotated to verify the
Institute (MNI), and Hilbert transform-base consistency of the proposed methods across detection approaches. We adopted previously validated
HFO detection parameters from published work (Zhang et al. 2025a). The complete parameter settings are summarized Table
Table Detector parameters used our annotation pipeline, All frequency units are in Hz, time in seconds unless noted.
STE Detector
MNI Detector
Hilbert Detector
Parameter
Value
Parameter
Value
Parameter
Value
filter freq
[80, 300]
filter freq
[80, 300]
filter freq
[80, 300]
mms window
3 10
epo CHF
60
sd thres
5
min_window
6 10 -3
CHF (%)
95
min_window
10 10
mip_gap
10 x 10
min_win
10 x 10-3
epoch 3600
min_ose
6
min_gap
10 x 10-3
mmsttres
5
thrd_pere
99.9999%
peak thres
3
hase seg
125 x 10-
epoch len (ms)
600
base shift
0.5
base_thrd
0.67
base_min
epoch_time (ms)
10
Annotation Procedure. Annotations were conducted by four board-certified clinical epileptologists, each with extensive experience epilepsy surgery and iEEG interpretation. All experts are actively involved in clinical epilepsy research, including patient diagnosis and surgical planning, ensuring that the annotations reflect high clinical standards and real-world applicability.
To standardize and streamline the workflow, all iEEG recordings were resampled to 1 kHz, ensuring a consistent temporal resolution that facilitates filtering, feature extraction, and cross-patient compar- isons. Bipolar montage is applied to the Zurich, HUP, and Sourcesink recordings. Candidate HFO events are first detected using PyHFO, and each event is subsequently reviewed and labeled by the experts either artifact, non-spKHFO, spkHFO.
To ensure inter-rater reliability, the experts first collaboratively reviewed a shared subset of sam- ple events to establish clear labeling criteria and achieve preliminary consensus. Following this calibration, each annotator independently labeled their assigned data subsets.
To further evaluate consistency, three primary annotators (R1-R3) independently labeled shared validation set of 200 events. Inter-rater agreement was high, with a Fleiss' к = 0.92 across the three annotators. Pairwise Cohen's ranged from 0.88 to 0.93 (Table 7 A complementary assessment
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between R4 and R3 on 583 events showed similar reliability (Cohen's 0.90 for artifact vs. non-artifact; x = 0.82 for spk vs. non-spk).
Table 7: Inter-rater agreement among the three primary annotators. Pairwise agreement is measured using Cohen's 's K, and overall agreement is measured using Fleiss' K.
Annotator(s)
Metric
Value
R1 vs. R2
Cohen's k
0,9427
R3 vs. RI
Cohen's k
0.9243
R3 vs, R2
Cohen's k
0,9091
R1, R2, R3
Fleiss' K
0.9254
C EXPLORATORY TASKS
C.I. ANATOMICAL LOCATION IDENTIFICATION
C.I.1 MOTIVATION AND CLINICAL BACKGROUND
Accurate anatomical localization of iEEG electrodes is critical for both clinical decision-making and neuroscientific research. Traditionally, this process involves co-registering post-implantation CT scans with pre-implantation MRI images, followed by manual or semi-automated labeling using neuroimaging software tools (Blenkmann et al. 2017 Lucas et al. 2024). While effective, these methods are time-consuming, resource-intensive, and may not be feasible in all clinical environments due to the need for imaging infrastructure and expert interpretation. As a result, some publicly released iEEG datasets do not include electrode-level anatomical annotations, limiting their utility for downstream physiological and clinical analyses.
Anatomical localization plays vital role in interpreting physiological patterns in iEEG data. Previous work (Guragain et al., 2018) has shown that certain electrophysiologica events exhibit region-specific distributions: for example, physiological HFOS are predominantly observed in occipital regions, while sleep spindles typically originate from fronto-parietal areas (Andrillon et al. (2011). More critically, in the clinical context of epilepsy surgery, localizing electrodes relative to eloquent cortices, such as those supporting motor, language, or memory functions, is essential for preserving key functions during resection. Besides the aforementioned biomarkers located in different anatomical locations, multiple studies have demonstrated that rich interictal electrophysiological signatures are specific to anatomical brain regions. Cortical areas such as the hippocampus, frontal lobe, and occipital lobe exhibit characteristic spectral and complexity features that are reproducible across patients (Frauscher cet al. 2018a Groppe et al.] 2013). These region-specific patterns are often clinically observable, and prior work has shown that even short segments, as little as five minutes, of interictal data can be sufficient to distinguish between regions (Frauscher et al. 2018a). Building on these insights, recent machine learning methods have demonstrated promising classification of anatomical regions using only signal-based features, showcasing the feasibility of non-imaging-base baased brain mapping (Taylor) et al. (022). Thus, the ability infer anatomical locations from electrophysiologica. recordings holds significant potential not only for enhancing physiological interpretations but also for informing clinical decisions, particularly in datasets that lack explicit anatomical metadata. Motivated by these findings, we propose an anatomical location identification task to benchmark and encourage further development of con-imaging--aar based localization approaches.
1.2 TASK DEFINITION AND EVALUATION METRICS
Task Definition. Inspired by above findings, this task aims to predict the anatomical location of iEEG electrodes based on one minute of interictal signal per channel. We formulate this as supervised classification problem at two levels of spatial granularity. The first level involves coarse- coarse-grained classification into five major anatomical regions: frontal, temporal, parietal, limbic, and occipital lobes. The second level refines this prediction to twelve fine-grained subregions, including Cingulate, Motor/premotor, Occipital, Mesial temporal, Inferior temporal, Inferior parietal, Prefrontal, Somatosensory, Superior parietal, Superior temporal, Insula, and Other/Subccrtiial.
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Evaluation Metrics. Since anatomical localization is performed at the channel level, similar to the pathological localization task described in the main manuscript, we adopt a channel-wise aggregation strategy to evaluate model performance. Specifically, we average the model-ppedicter probabilities across all 1-minute windows for each channel, yielding an overall probability distribution over anatomical locations (major: classes; fine: 12 classes) per channel. Performance is then assessed using standard classification metrics over both label granularities. We report balanced accuracy (Acc) and aaaro--aeraaged Fl score (F1), which are robust class imbalance and reflect performance across all anatomical regions.
Cohort Summary. Among the datasets considered, only the Open-iEEG dataset includes anatomical labels for individual channels. Therefore, this downstream task is restricted to two research centers within the Open-iEEG dataset, following the patient-wiss cross-validation split outlined in Section of the main manuscript. Specifically, the anatomical localization task involves 185 patients and 18517 candidate channels, each with known anatomical labels obtained through imaging co-registration and expert annotation.
C.2 ICTAL PERIOD CLASSIFICATION
C.2.1 MOTIVATION AND CLINICAL BACKGROUND
Accurate identification of ictal periods, moments when seizures are actively occurring, is fundamental to epilepsy diagnosis and treatment planning. Clinically, the transition from interictal to ictal states provides crucial insight into seizure dynamics, including onset, propagation, and termination (Wendling et al. 2005). However, ictal events can be brief, subtle, and easily overlooked in lengthy recordings (Pyrzowski et al. 2021). Manual review by experts is time-consune consuminn and may miss atypical subclinical seizures, which increasingly recognized clinically relevant (Fisher et al., 2014| Alhilani et al.) 2020; Sumsky & Greenfield Jr| [0222] Kharbouch et al. (011). An automated system that reliably distinguishes ictal from interictal activity offers not only diagnostic support and improved consistency across reviewers and institutions but also has the potential to reduce overall monitoring duration and alleviate the burden on clinical staff.
Moreover, recent advances in neuromodulation therapy, such as responsive neurostimulation (RNS), highlight the need for real-time, fine-grained seizure detection (Sun al. 2008). Clinicians have noted that identifying precise ictal boundaries could help define personalized stimulation targets, enabling therapeutic interventions occur the earliest possible seizure onset (Boddeti et al. 2022). In this context, accurate ictal detection is not merely retrospective; it becomes cornerstone for proactive, closed-loop treatment systems. Therefore, we view ictal period identification as a clinically actionable task, bridging scizure monitoring with intervention and offering a foundation for both diagnostic and therapeutic innovation.
C.2.2 TASK DEFINITION AND EVALUATION METRICS
Task Definition. The goal of this task is distinguish between interictal and ictal periods iEEG recordings. The ictal recordings are contained in the HUP dataset, but the interictal recordings could be retrieved from all patients from the Omni-iEEG dataset. For each patient, we segment the data into one-minute clips and formulate the problem as a binary classification task: given one-minute iEEG segment from all channels, predict whether it originates from an ictal interictal recording. This formulation ensures standardized input length while reflecting realistic clinical scenarios where seizure identification must occur from streaming or windowed recordings.
Evaluation Metrics. Since our baseline models operate on ID signals while the task is defined at the recording level (classifying whether a multi-channel iEEG recording corresponds to an ictal or interictal state), we adopt a straightforward aggregation strategy. Due to the varying number of channels across patients, which complicates direct cross-channel neural network training, we compute the average predicted probability across all channels for each 1-minute window in the test set. This aggregated probability is treated as the model's 's prediction for the ictal/interictal state of that window. We then determine the optimal classification threshold using the area under the ROC curve (AUC) and the Youden's 's method. Using the binarized predictions from this threshold, we evaluate model performance using balanced accuracy (Acc) and macro-averaged F1 score (F1), which account
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for class imbalance and provide comprehensive assessment of performance across both ictal and interictal classes.
Cohort Summary. This task includes 16 patients in the training set and 9 patients in the test set. Notably, the interictal patients are uniformly sampled from each data source. The training set includes 6205 unique channels, while the test set comprises 3561 unique channels. To prevent overrepresen- tation from long recordings, we apply stratified sampling during training to draw windows evenly across channels.
C.3 SLEEP AWAKE CLASSIFICATION
C.3.1 MOTIVATION AND CLINICAL BACKGROUND
Accurately identifying sleep and awake states in iEEG is essential for both clinical care and research. The vigilance state patient strongly influences the manifestation of seizures and interictal dis- charges; for example, frontal lobe seizures are more likely to occur during sleep, particularly in non-REM stages, whereas temporal lobe seizures are more frequent during wakefulness (Herman et al. 2001). In long-term recordings like SEEG, knowledge of the sleep-awake cycle can aid in distinguishing epileptic from non-epileptic events (Bazil Walczak 1997). From clinical perspec- tive, this information improves diagnostic precision and guides personalized treatment strategies, especially when manual annotations are inconsistent or missing (Moore et al., 202I). Moreover, sleep-awake identification is often more challenging in iEEG than in scalp EEG, where awake states are typically accompanied by distinct artifacts that assist visual classification (Lambert & Peter-Derex 2023). In contrast, iEEG recordings lack such clear surface-level markers, making automated approaches particularly valuable (Lambert & Peter- Derex, 2023). Automated sleep staging enables efficient parsing of long iEEG recordings, reduces annotation burden, and helps identify longer usable segments for downstream analysis (Derry & Duncan 2013).
Sleep-awake classification extends beyond clinical utility, enabling consistent, automated labeling of vigilance states across large iEEG datasets. This supports standardized analysis of sleep-dependent modulation of interictal biomarkers like high-frequency oscillations, facilitating cross-patient com- parisons and advancing scalable, reproducible brain state analysis in epilepsy research (Zhao et al. 2024
C.3.2 TASK DEFINITION AND EVALUATION METRICS
Task Definition, The goal this task to distinguish between sleep and awake periods in iEEG recordings. The awake recordings are in the Sourcesink dataset, but the sleep recordings can be retrieved from all patients in the Omni-iEEG dataset. For each patient, we segment the data into one-minute clips and formulate the problem as binary classification task: given one-minute iEEG segment, predict whether it originates from sleep or awake recording. This formulation ensures a standardized input length and aligns with real-world clinical needs, where vigilance state identification must often be performed from streaming or windowed data.
Evaluation Metrics. Similar to the ictal/interictal classification task, the sleep/awake classification also performed on multi-channel iEEG recordings. We adopt the same evaluation strategy, aggregating predictions across channels for each 1-minute window. Model performance is assessed using standard binary classification metrics, including balanced accuracy (Acc) and macro-averaged Fl score (F1), which provide a fair evaluation across both sleep and awake states.
Cohort Summary. This task includes 13 patients in the training set and 8 patients in the test set. The interictal patients are uniformly sampled from each data source. The training set includes 1751 unique channels, while the test set comprises 802 unique channels. We also apply stratified sampling during training to draw windows evenly across channels.
D EXPLORATORY TASKS BASELINE RESULTS
The detailed benchmark results for all three tasks across baseline models, SEEG-NET, CLAP, and CNN, are presented in Table & Interestingly, the foundation audio embedding model CLAP often achieves strong performance across tasks, and the end-to-end CNN also demonstrates competitive
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Phystolgrca
Figure 2: Event-based iEEG input: raw waveform (left) and time-frequency representation (right).
302 Presevee
Insected Presevedd not 2011
Figure 3: Channel Segment- based iEEG input: raw waveform (left) and fimeufreyuency representation (right).
results, consistent with the trends observed in the main manuscript. We hypothesize that the generally better performance these models reflects their stronger capacity to capture relevant signal represen- tations. This suggests that the neurophysiological biomarkers underlying these tasks embedded in the morphological characteristies of the iEEG signal, supporting the relevance of our proposed dataset and task formulation.
For SEEG-NET, since the authors did not release their implementation or dataset, we reproduced the architecture based on the details provided in the paper. However, the resulting performance was suboptimal, which may be attributed to potential discrepancies between our implementation and the original, given the absence of released code and data. This further highlights the importance of benchmark, which offers a publicly available, reproducible platform for developing and evaluating models in this domain.
Table 8: Macro-averaged Fl and balanced accuracy scores for the Anatomical with 5 Classes and 12 Classes, letal/Interictal, and Sleep/Awake classification tasks.
Model
Anatomical(5)
Anutomical( 12))
Ictal
Sleep-Awake
F1
Acc
F1
Acc
F1
Acc
Fi
Acc
SEEG NET Wany et 2022
0.2520
0.2550
0.1081
0.1853
0.7526
0.7624
0.6773
0.6421
CLAP et al 2025h
0.4750
0.4894
0.3540
0.3897
0.9245
0.9323
0.7225
0.9331
TimeCon-UUNN
0.4788
0.4708
0.3087
0.3215
0.8533
0.8720
E IMPLEMENTATION DETAILS
E.1 TIME-RREQUENCY REPRESENTATION
Inspired by prior interdisciplinary studies (Zhang et al.) 2022at Ding et al., 2025). we adopt the fime-freyuency representation (Spectrograms) of iEEG signals using Morlet wavelet transforms
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capture the temporal dynamics of each frequency band. This representation has been shown to provide a comprehensive view of signal morphology and demonstrated superior performance in both previous literature and our experiments. As illustrated in the example time-frequency representation of HFO events (Figure 2 and different iEEG channels (Figure 3). distinct morphological features in the iEEG tracing become more distinguishable in the time-frequency domain. Moreover, the inherent parallelism of time-frequency computation enables efficient processing on GPUs, sigmificantly accelerating feature extraction. Our implementation of this process is available in the accompanying code repository. For downstream tasks, we follow established practices from prior work (Zhang et al. 2024), which indicate that the most informative content in iEEG signals lies within the 10-300 Hz range when the downstream models take the time-frequency representation as inputs.
E.2 EVENT-BASED MODELS.
Data Construction. For each labeled HFO event, we extract a 570 ms iEEG segment centered at the event midpoint. To ensure consistency across recordings, include only data with minimum sampling rate of 1000 Hz and resample all inputs to 1000 Hz. These standardized segments serve as the input to all baseline models.
PyHFO-O Onron-OOniii Architecture. Following the architecture of the HFO classification model introduced in PyHFO (Zhang al.) 2024), we retrain the model using Omni-iEEG annotation. Specifically, we use convolutional neural network based on a modified ResNet18 8 backbone for event-level classification. The input spectrograms are passed to ResNet18 encoder, where the first convolutional layer is adjusted to accept single-channel input, and the classification layer produces the logit of each
class.
Attention+LSTM Architecture. Inspired by Huang et al. (2023). implement a bidirectional LSTM model for event-level classification on raw iEEG signals. The input sequence first processed by two-layer bidirectional LSTM to generate hidden states each timestep. To summarize the temporal dynamics, we compute a weighted sum of the hidden states using learned additive weights. This aggregated context vector is then passed through classification layers to produce the final output logits.
PStchTST Transformer Architecture. We implement PatchTST-style (Nie et al.) 2023) Trans- former model for raw iEEG signal classification. Each ID input sequence is split into non-overlapping patches, which are linearly projected into a fixed embedding space. A learnable class token is prepended to the patch sequence, and positional encodings are added to retain temporal order. The resulting sequence processed by multi-layer Transformer encoder. The output embedding corresponding to the class token is then passed through classification head to produce the final logits.
TimesNet Architecture. We include TimesNet (wu et al. 2023a). a recent competitive time-series model that captures multi-periodic patterns using frequency- aaware design. The model first embeds the input sequence and passes it through multiple stacked modules, each identifying dominant periods via FFT and applying 2D convolutions on reshaped periodic representations. These outputs are adaptively aggregated based on learned frequency weights. We adopt the official implementation without modification and apply directly to our event-level iEEG classification task.
For comprehensive details model architecture and parameter configurations, please refer to released codebase.
E.3 SEGMENT-BASED MODELS.
SEEG-Net Reproduction. We implement SEEG-Net, a multiscale and attention-based deep learning architecture originally proposed for pathological activity detection in SEEG signals (Wang et al.) 2022). The model begins with a multiscale convolutional block composed of three parallel ID convolutional branches with varying kernel sizes, each capturing frequency-specifie features at different temporal resolutions. Outputs from all branches are concatenated and passed through residual ceeeeez--nddeexiitte (SE) block to enhance the most informative feature channels. This representation is then fed into a bidirectional LSTM with an attention mechanism to model long-range temporal dependencies. Finally, feedforward classifier outputs a binary decision. Our
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implementation follows the core architectural design of the original SEEG-Net and adapts it for segment-level iEEG classification task.
CLAP-Based Model. To explore pretrained audio-language representations for iEEG classification, we adapt the CLAP model (Wu et al. 2023b), originally trained on natural audio-text pairs, for our long-form iEEG recordings. Specifically, and attach a lightweight classification head. Raw iEEG waveform segments are processed using the CLAP feature extractor with adjusted hyperparameters. We conduct full-parameter fine-tune of CLAP with a classification head. This setup leverages CLAP's pretrained acoustic representations for downstream iEEG classification tasks.
employ the pretrained clap-htsat-fused checkpoint
TimeConv-CNN Architecture. Long iEEG recordings yield extremely large time- frequenyyy repre- sentations, which are rarely modeled directly due to their prohibitive scale. To address this challenge in clinically meaningful setting, we design T'imeCon-CNN architecture that adapts established vision backbones to the unique demands of iEEG. The model begins with two time-wise convolutional layers, which apply vertical (temporal-only) convolutions across the spectrogram. This design specif- ically targets the elongated temporal axis, compressing long sequences while preserving detailed spectral structure that is critical for identifying pathological dynamics. The reduced representation is then consumed by a modified ResNet18 backbone, where the first convolutional layer is adapted to match the reshaped feature dimensions. This allows to leverage the hierarchical feature extraction of ResNets without overwhelming computational resources. Finally, a classification layer produces the output logits. We adapt axis-specific convolutions practical strategy to model minute-long kilohertz-sampled iEEG segments end-to-end. Temporal convolutions efficiently compress the elon- gated time axis, and the ResNet backbone captures higher-order spatiotemporal patterns, enabling scalable modeling of long iEEG recordings.
For comprehensive details on model architecture and parameter configurations, please refer to our released codebase.
E.4 TRAINING HYPERPARAMETER
Our training setup varied slightly depending on the model type: event-based models were trained for 20 epochs, while segment-based models were trained for 10 epochs due to computational constraints. We used the Adam optimizer with a learning rate of 0.0003 and a batch size of 32, To address class imbalance, we employed weighted random sampling during training. Binary classification tasks used Binary Cross-Entropy loss, while multi-class tasks were trained with Cross-Entropy loss. Training data are processed if necessary, such as converting to time- freeufryy representations, before being fed into the model. Validation was performed after each epoch, and the best model checkpoint was selected based on validation macro-F1 score. Note that we do not perform extensive hyperparameter tuning; instead, we adopt a unified and simple configuration across experiments to ensure consistency and reproducibility.
E.5 COMPUTE RESOURCES AND REPRODUCIBILITY.
All experiments were conducted on server equipped with 4 NVIDIA RTX A6000 GPUs, each with 48 GB of memory. Training time varies by model type: event-based models typically converge within 10-30 minutes, while segment-based models require approximately 1-2 hours depending on signal duration and batch size. All training evaluation steps were executed on the same hardware. Please refer to our released codebase for full implementation specifics, including model architectures, layer dimensions, training hyperparameters, and preprocessing pipelines.
performed using single GPU. Preprocessing and
F ADDITIONAL EXPERIMENTS AND ANALYSES
F.I ABLATION STUDY ON DASSS-DTTASTT GENERALIZATION
To ensure that the observed performance is not driven by dataset-specific patterns, we performed three complementary analyses: (i) leave-one-dataset-ont experiments the HFO event classification
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level: (ii)
experiments for pathological brain region identification; and (iii) permutation test on channel-level embeddings to evaluate potential dataset-specific clustering.
Leave-one-dataset-out HFO event classification. We evaluate model performance in cross- dataset generalization setting using the HFO event classification task with leave-one-dataset-ov strategy. The model is trained on all datasets except one and evaluated on the held-out dataset, with Fl score as the primary metric. The diversity and comprehensiveness of the cohort contribute to the model's 's generalization ability. As shown in Table holding out the Open-iEEG dataset results in performance drop relative to holding out other datasets, likely due to its larger size and greater diversity, which better support model generalization HFO classification.
Table Cross-dataset generalization via lave-one-dataset-out HFO event classification.
Held-Out Precision
Recall
F1
Open-iREG
0.6955
0.6893
0.6227
Zurich
0.7342
0.7520
0.7416
HUP
0.6967
0.7647
0.7219
SourceSink 0.7110
0.7412
0.7221
Leave-one-datasst-out pathological brain region identification. We additionally evaluate the pathological brain region identification task under leave-one ont-dattset-out setting. Note that not all centers can be meaningfully included: the Zurich dataset does not contain SOZ annotations, and HUP includes only 13 surgical patients, making outcome evaluation statistically unstable. We therefore report two leave-one-dataset--ou configurations: (i) Open-iEEG (185 patients) as the held-out dataset and (ii) Source-Sink (33 patients) the held-out dataset. We follow the same evaluation protocol as in the main paper: all treesholdddeeendentt metrics (Precision, Recall, F1, Specificity) use the same fixed global threshold of 0.5, and threshold-free metrics (AUC and Outcome) are evaluated identically. This ensures full consistency between the leave-one ceave-one-dataset--o setting and the pooled-cente benchmark. As shown in Table T0] relative performance across models remains consistent across centers; the absolute performance (especially in outcome prediction) varies due to variation in the number of seizure-free patients (Open-iEEGG: 51, Source-Sink: 18), and our proposed domain-transfer model, CLAP, and TimeConvCCNN, demonstrate competitive performance under these two ablation studies.
Table 10: Cross-dataset generalization via leave-one-datasst-ouu pathological brain region identifica- tion.
Held-Out
Model
Precision
Recall
F1
Specificity
AUC
Outcome
TimeConv-CNN
0.5912
0.6393
0.5936
0.7383
0.6983
0.5325
Open-iBEG
CLAP
0.5785
0.6207
0.5781
0.7284
0.6560
0.5122
SEEGNET
0.5746
0.6335
0,5279
0,5608
0.6650
0.4628
TimeConv-CN
0.5995
0.5857
0.5902
0.8392
0.6031
0.7740
Source-Sink
CLAP
0.6109
0.6063
0.6083
0.8168
0.6222
0.7592
SEEG-NET
0.6965
0.5999
0.6100
0.9430
0.6331
0.7740
Permutation test on channel-level representations. In addition the leave-one-dataset-outt evaluations, we examine whether channel-level data distributions exhibit strong dataser-specific separation. which could confound pattern discovery. To examine this, we sample 500 one-minute segments per dataset (2000 total), generate time fineffreyynncy plots, and extract embeddings with ViT backbone (goog wwwit-baae-pate..o groups using K-means clustering and quantify alignment with dataset labels via homogeneity score. To assess whether any observed alignment could arise by chance, we repeat the test with 1000 random permutations of dataset labels. The observed alignment is indistinguishable from chance (p=0.88), indicating that there no significant evidence of distributional differences for channel-Jevel representations across datasets. This complements the leave-one soave-one-dttset-onn analyses: while the leave-one-ous analysis demonstrates generalization in predictive performance, the permutation test verifies that channel-level embeddings not confounded by dataset distributional differences.
6-224). We then cluster embeddings into k=4
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F.2 ABLATION STUDY ON CHANNEL-LEVEL SEGMENT LENGTH
To justify the usage of 1-minute segments in our channel-level tasks, we evaluate the impact of segment length through an ablation study using the Cinneonn-CCNNbaasee segment classification model with 30-second, 1-minute, and 2-minutes inputs. Evaluation is restricted to test samples with least 2 minutes of data to ensure all configurations have sufficient input length, which is a different cohort of our original cohort. Longer segments (2 min) also yield very few test windows, making resection ratio estimates unstable, so we report per-channel classification using only the channel-level labels. Segment length influences both context and stability. As shown in Table [T] very short segments (30 seconds) lack sufficient context for reliable classification, while very long segments (2 minutes) dilute fine-gras sene-graindd temporal features, A I-minute segment provides a practical trade-off, capturing meaningful neurophysiological features while maintaining stable evaluation.
Table 11: Segment length ablation study (pathological channel classification).
Model
Precision
Recall
F1
Specificity
AUC
TimeConv-CNN (30
0.5768
0.7072
0.5442
0.6592
0.7729
TimeConv-CND (1 min)
0.6081
0.7607
0.6096
0.7478
0.8229
TimeConv-CNN (2 mins)
0.5919
0.7473
0.5642
0.6684
0.8052
F.3 ABLATION STUDY ON SAMPLING RATE
Resampling all recordings to 1000 Hz. is a design choice in our benchmarking pipeline to standardize inputs across datasets while preserving spike morphology and high-freeuency structure. To assess robustness to this parameter, we performed sampling rates are not clinically meaningful for intracranial EEG interpretation. As an ablation, we additionally resampled all recordings to 500 Hz and retrained the segment- based pathological region identification task. As shown in Table 12, despite the reduced resolution, the relative performance ranking across methods remains consistent with our primary results (compared with Table indicat- ing our conclusions are robust to this parameter. Note that HFO-based methods cannot be evaluated at 500 Hz, the sampling rate violates the Nyquist limit for ripple/fast-rippl bands. Thus, only non-HFO models were included in this ablation.
ablation on the resampling rate, even though lower
Table 12: Ablation on resampling rate for pathological brain region identification (500 Hz).
Model
Precision
Recall
F1
Specificity
AUC
Outcome
TimeConv-CNe
0.5924
0.7192
0.5884
0.7371
0.7735
0.7113
CLAP
0.5997
0.7191
0.6065
0.7740
0.7843
0.7048
SEEGNET
0.5919
0.7339
0.5744
0.6946
0.8076
0.6364
F.4 MODEL INTERPRETABILITY ANALYSIS
Beyond raw predictive performance, model interpretability is essential in clinical applications where trust and adoption depend on understanding whether predictions are based on physiologically meaning- ful features. In particular, identifying which frequency bands drive model decisions helps determine whether the network captures established neurophysiological correlates of HFOs instead relies on spurious artifacts. To this end, we conduct a SHAP analysis to examine which frequency bands most influence the TimeConv-CNy CCNN model's predictions. As shown in Figure 4 we find that activity in the 10-30 Hz range contributes most significantly to the model's spkHFO predictions. This observation aligns with clinical knowledge, spike components typically concentrated within this frequency band. The agreement between the model's behavior and known neurophysiological features suggests that the model is learning meaningful patterns rather than spurious correlations.
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HFO frequency Importance
Artfact Frequency Importance
sptriO frequency Importance
100
109
809
Frgquncc 2441
2643
100
1.9
0.9
171
771
204
Maan 1SF Value]
Maaan SSH (aluel]
Plaan [SHSP Vakue]
Figure 4: Model interpretation analysis using SHAP.
F.5 ADDITIONAL BASELINES FOR PATHOLOGICAL BRAIN REGION IDENTIFICATION
To provide additional reference points beyond the main benchmarks, we include two additional baselines: an adapted EEGNet model and a linear-feature baseline. However, these basclines are not a perfect fit for iEEG settings.
EEGNet adaptation. EEGNet (Lawhern al. 2018) originally developed for scalp EEG, where recordings follow a fixed multi-channel montage and consistent spatial layout, and thus its original design does not directly transfer iEEG. Because iEEG differs substantially in electrode configura- tion, spatial organization, and montage variability across patients, we adjust EEGNet to operate single input channel. This modification aligns with our pathological brain region identification task, where the goal is to determine whether each individual iEEG segment is pathological or not.
fandcrafted-featurre baseline. We additionally provide conventional machine-learnngg baseline built on handcrafted features extracted directly from the raw time-domain signal. These include standard time-domain descriptors (mean, median, variance, standard deviation, root-mean-square cero-ccossing rate, Hjorth activity/mobility/commpppt
amplitude, line length, zero-crosse features derived from a Welch-style PSD estimate (spectral entropy, peak
skewness, and kurtosis)
and frequency-domall frequency, total spectral energy, and log-bandpower across canonical bands: 8 (0.5-4 Hz), 0 (4-8 Hz), a (8-13 Hz), /3 (13-30 Hz), 7 (30-80 Hz), and high-y (80-150 Hz)). These features have long been reported iEEG signal analysis. We train a simple one-layer MLP with sigmoid output on these features. Table 13 shows that these baselines do not close the gap with the stronger models introduced in the main manuscript. Although the linear baseline achieves competitive outcome- predictonnr performance in this setting, its specificity limits practical clinical value. Overall, both EEGNet and the linear model fall short more competitive architectures such SEEG-NET, CLAP, and TimeConv-CNN.
Table 13: Additional baselines for pathological brain region identification (Task 2).
Model
Precision
Recall
F1
Specificity
AUC
Outcome
SEEG-NET
0.5790
0.7169
0.5259
0.6049
0.7850
0.5952
CLAP
0.5936
0,6997
0.6009
0.7823
0.7684
0,6770
TimeConv-CNN
0.6259
0.7454
0.6469
0.8230
0.8061
0.7380
EEGNet (adapted)
0.5792 0.5323
0.6957
0.5640
0.7075
0.7468
0,5942
Linear baseline
0.5045
0.4899
0,9880
0.5045
0.4920
G LIMITATIONS
Subjectivity and Variability in Annotations. While all HFO event annotations are labeled and reviewed by board-certified clinicians, inter-rater variability remains well-recognized challenge.
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For example, there is still no universally accepted definition of spkHIFss, which may introduce biases or inconsistencies in model training. Moreover, variations in labeling protocols across institutions further increase annotation variability. These sources of uncertainty underscore the need for robust, interpretable models that can operate reliably under noisy supervision.
More Diverse Patient Cohort While the Omni-iEEG dataset represents one of the most comprehen- sive collections invasive EEG recordings to date, curated from multiple leading epilepsy centers, does not fully capture the global heterogeneity of epilepsy presentations and surgical practices. Important dimensions of diversity, such as broader demographic representation and varied pathology, remain underrepresented. Expanding future datasets to include a wider array of clinical contexts and patient populations will be critical for developing truly generalizable machine learning models.
Dataset Imbalance and Biases. Due to the inherently clinical nature of the dataset, achieving balanced representation across categories and conditions is challenging. For instance, SOZ channels are substantially outnumbered by non-SOZ channels, and certain anatomical regions or biomarker types are disproportionately represented based on clinical implantation practices. Although we introduced evaluation metrics and training strategies to mitigate these imbalances, such disparities can still bias the model toward overfitting to data-rich conditions or favoring dominant classes. These biases may limit model generalizability, particularly when deployed in settings with different clinical practices or patient populations.
Potential Algorithmic Over-reliance. critical limitation lies not in the model performance itself, but in the potential misuse of machine learning systems trained Omni-iEEG, particularly when deployed without proper human verification. Abuse of trained models in clinical workflows, such as surgical planning, could lead inappropriate and potentially harmful decisions. Over-reliance on model outputs, especially without clinician oversight, may lead to severe consequences. Responsible use demands rigorous validation, model transparency, and active involvement of clinical experts in the decision decision-making process.
H FUTURE WORK
To address the limitations discussed above, our future work will pursue several key directions. First, plan to expand the benchmark by incorporating additional datasets they become publicly available or once new data collections receive IRB approval. This will enhance the diversity and generalizability of our findings. Second, we aim evaluate a broader range of baseline methods, including unsupervised approaches that leverage graph structures and inter-channel correlations to capture latent spatial and temporal patterns in iEEG recordings.
Third, we intend to involve wider pool of annotators from diverse academic and clinical backgrounds label additional pathological events, using multiple biomarkers such as interictal spikes. This will enrich the dataset and improve the robustness of supervised and semi-supervised learning tasks.
Finally, while current benchmarks focus primarily on predictive performance, clinical deployment demands models that are not only accurate but also interpretable. Epilepsy surgery planning typically involves multi-disciplinary review, where transparency and trust in algorithmic outputs are critical. Even through we introduced interpretation analysis in this study, we plan to develop a more com- prehensive explainable AI frameworks specifically designed for iEEG data, enabling clinicians to interpret model decisions and integrate them meaningfully into the diagnostic workflow.
I DETAILED DATASET SPECIFICATION AND HARMONIZATION
Data Processing and Harmonization. We follow the preprocessing steps recommended in prior publications (Fedele et al.) 2017; (unnnasdotttir al., 2022 Zhang et al. 2025a Bernabei al. 2023a) (e.g., applying bipolar montages where appropriate). All recordings are reviewed by board- certified clinicians to ensure they meet the quality standards necessary for epilepsy research. A systematic validation process checks the consistency between channel annotations and raw data, resolving misalignments or inconsistencies in channel naming conventions across datasets. Clinicians also review the complete channel lists, filtering out non-standard EEG recordings (e.g., reference, ground, EKG, or stimulation channels) and excluding those that consistently exhibit non- -oyysiiooloiiaal patterns such flat signals excessive noise. These filtering decisions are grounded in established
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clinical conventions and expert judgment steps that are straightforward for clinicians but non-trin non-trivial for researchers without specialized training.
Beyond channel-level review, we unify and clean the metadata across sources, which vary considerably in channel annotations, surgical status, and outcome reporting. At the subject level, we include standardized attributes such as age, gender, surgical status, and postoperative outcomes, while the channel level we provide detailed metadata including sampling frequency, modality (ECoG or SEEG), recording quality (with corrupted channels marked as bad), total duration, seizure onset zone (SOZ) status, resection status, and anatomical location. Together, these harmonization and quality-control steps yield ready-to-use iEEG resource that bridges the clinical and machine learning communities.
Contributing Datasets Overview. For each dataset, we describe its origin, patient cohort char- acteristics, recording modalities, sampling frequencies, durations, and the availability of clinical annotations such as SOZ, resection zones, and surgical outcomes. We also detail the preprocessing steps undertaken to ensure consistency and data quality, as well as the strategy used for train- ing/testing partitioning. This breakdown aims to enhance transparency and facilitate reproducibility for researchers using the Omni-iEBG dataset.
Open iEEG Dataset. The Open iEEG Dataset (Zhang et al., 2025a) includes iEEG recordings from two institutions: UCLA Mattel Children's Hospital (50 patients) and Children's Hospital of Michigan, Detroit (135 patients). Recordings from Detroit are exclusively ECoG, sampled at 1000 Hz, with durations ranging from around 5 to 10 minutes. Recordings from UCLA include both ECoG and SEEG, sampled at 2000 Hz, with recording durations ranging from 10 minutes up to 2 hours. Most patients have annotated seizure onset zones (SOZ), resection zones, and surgical outcomes. All recordings were acquired during the interictal sleep period. In addition, each channel is annotated with its corresponding anatomical location, enabling the dataset's 's use in the Anatomical Location Prediction tasks, and supporting future -specoiccc analyses.
Zurich iEEG HFO Dataset. The Zurich iEEG HFO Dataset dataset (Fedele et al. 2017) (Zurich) consists of recordings from 20 patients collected at the University Hospital Zurich. All recordings are ECoG sampled at 2000 Hz. Each patient has approximately 10-20 recordings of 5-10 minutes each. 15 patients have valid resection annotations, while none have SOZ annotations. All recordings were captured during interictal sleep stages. Resection annotations and channel status information were carefully extracted from the original publication. Following the methodology described in the original study, we also applied bipolar montage during preprocessing.
Epilepsy Interictal Dataset (SourceSink). The Epilepsy Interictal Dataset dataset (Gunnarsdottti et al. 2022) comprises 39 patients collected across three institutions: National Institutes of Health (NIH), Johns Hopkins Hospital (JHH), and University of Miami Florida (UMF). This dataset includes both ECoG and SEEG recordings, with sampling rates ranging from 500 Hz to 2000 Hz. All patients have SOZ, resection zone, and surgical outcome annotations. A board-certified clinical expert reviewed the recordings and applied bipolar montage to reduce common-most mode noise. The dataset contains both sleep and awake recordings. making it suitable for tasks such as Sleep/ Awake Classification. For our main benchmark, we select only recordings acquired during sleep and sampled above 1000 Hz.
HUP Dataset. The HUP dataset (Bernabei et al. 2023a contains recordings from 58 patients treated at the Hospital of the University of Pennsylvania The dataset includes both ECoG and SEEG recordings. All patients have SOZ, resection zone, and surgical outcome annotations. A board-certified clinical expert reviewed the recordings and applied a bipolar montage to reduce common-mode noise. The dataset contains both ictal and interictal recordings, making suitable for Ictal Period Classification tasks. For our main benchmark, we include only the interictal segments with at least 1000 Hz.
Note that all datasets are collected with necessary IRB approval to ensure ethical standards. We include a comprehensive table below summarizing metadata for all the patients. For each subject, we report demographic information (age, gender), clinical annotations (SOZ, resection, surgical outcomes), inclusion in the training or testing split, and data availability indicators (presence of anatomical annotations, awake recordings, and ictal recordings). This summary offers detailed view of the cohort composition and facilitates targeted benchmarking or subgroup analyses. Please check Table [4 for details.
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Table 14: Summary of patient metadata and recording availability. Column abbreviations: SO Surgical Outcome; SOZ. Seizure Onset Zone Annotation; Res Resection Annotation; Anat Anatomical Annotation; Bv = Event Annotation; Int = Interictal Recording; Ict= Ictal Recording: Awk Awake Recording. Value abbreviations: M Male; F Female; Yes (present); N No (absent); = Not applicable or not available.
Patient Name
Dataset
Age
Gender
SO
SOZ
Res
Anat
Ev
Int
Ict
Awk
Split
sub-hupFH/PO060
hup
HUb-b44
hup
21
M
Y
N
Y
sub hup5
hup
36
M
Y
Y
train
HUP-0
hup
33
M
Y
Y
N
Y
train
sub-bupHUP007
hup
25
F
Y
Y
N
Y
train
sub-buptiU/M175
hup
sub hupl]U/P800
hup
41
F
Y
Y
N
N
Y
Y
train
sub-hupHUP082
hup
Y
sub hUp//U
25
F
Y
Y
sub-hupHU/P087
hup
24
M
Y
Y
test
sub-hupHUPoS8
35
F
Y
Y
sub-bupHUrr089
hup
29
M
Y
Y
Y
N
Y
train
sub- sub-huplIUP0994
hup
F
N
sub huphh//91///97
hup
19
N
train
sub-hupHUP
hup
39
M
Y
sub P100
45
F
Y
train
sub-hupHPP107
hup
36
M
train
sub-hupHUP111
F
train
sub-bupHUP112
hup
21
train
sub hupl1UP111
hup
sub-hupHUP116
hup
59
train
sub-hupHUP117
hup
39
M
test
sub-hupH1/P123
hup
36
M
train
sub-hupHUP126
hup
26
train
sub-tupHUP130
hup
F
sub Sub-huptUUP1332
hup
47
test
sub-hupHH/P133
hup
sub-hupHUP134
hup
32
M
train
sub-hupHUP135
hup
37
M
test
sub-buHUUP138
38
M
train
sub-hupHUP139
hup
20
M
train
sub-tupHUP140
hup
sub hup1]UP141
hup
30
M
train
sub-hmpHUP114
hup
30
sub-bupHUP144
hup
31
M
train
sub-hupHUP146
hup
16
M
test
sub hup1
23
M
train
sub-bupHUP1500
hup
17
M
train
sub-uupHUPP151
hup
sub hupH1/P157
hup
25
M
train
sub-hupHUP158
hup
32
M
sut-bupHUP160
hup
45
F
train
sub-hupHUP162
hup
35
train
sub hup1
42
F
sub-bupHUP164
hup
34
F
test
sub hupt1UP165
hup
sub H1b-
hup
26
M
train
sub-hupHUP171
hup
50
M
train
sub-bupHUP172
hup
28
F
train
sub- sub-hupliUP173
hup
24
train
sub- P1b
hup
42
sub- sub-buptiUP179
hup
20
F
lest
sub hubhhu1
hup
sub-hupHUP18I
hup
31
F
train
sub-hupHUP185
hup
38
M
train
sub-bupHUP187
25
M
train
sub hupllt/P88
hup
24
F
train
sub-hupHU/P190
25
M
sub 1enteeeeeeeeeeeiii00000
openieey
12
M
Y
train
sub-ogenniegDDerriite
openiceg
subbogeenieegDemmni0003
openioog
10
M
train
sub-opoenleegeetoott044
openiseg
15
F
test
sub sub-opennieeDeetoiiiit
openieeg
M
train
suh wehheeeeiieg
openieeg
20
M
train
sub-opemieegDeetoon0007
openiceg
17
F
train
suh oehoogenneegggttniioon
openieey
6
M
N
test
sub-opennieegDetroinie
openicag
sub-opeenieegDeerro00000
sub-openieegDeeooit001
openieeg daamess
openicog
11
17
M
M
N
train
train
openieeg
13
Y
N
train
sul- 3
openiceg
train
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Patient Name
Dataset
Ago
Gonder
SO
SOZ
Res
Anat
Ey
Int
let
Awk
Spli
sulh Dett-pee
1
openiceg
14
M
Y
Y
N
Y
train
sub 1
openieeg
5
M
Y
Y
N
Y
N
train
suh- oph-ioeeneegg 16
openiceg
openiceg
19
F
Y
Y
Y
N
train
sub-openiieeDDeooinnn
openiceg
5
M
Y
Y
train
sub-ugernierDDetrii00
openieey
13
N
Y
N
train
suih igernieegDDttrooi00000
openiceg
9
F
Y
Y
train
sub-rgemieeeDDetooi0001111
openiceg
12
Y
Y
sub-oppeniceeD<<toint222
openiceg
10
M
Y
Y
Y
N
Y
train
sub-rgeeniceg@Demoo00021
openiceg
subboopeniceegDetroi00444
openicog
4
F
Y
Y
N
test
sub-openieeeDDeooo00255
openieeg
10
F
Y
train
ugeiieegDeerrv0022
openieey
16
M
train
suth
openiceg
15
Y
train
sub-ogenicegDetooirft
openiceg
16
Y
train
sub-openiieeDDerrittt22
openiceg
10
M
test
sub-ogeeniceg
openiceg
14
Y
sub-openniceDDetroittt
openiceg
7
train
sub- bubropenieeeDDeteii002
openieeg
28
test
openicey
17
train
sulb
aub-ogeniieeDeetonini
openiceg
17
test
sub-opemiccgeDetoonntts
openiecg
30
sub. sub-opennieeggDetviitttt
openieeg
10
M
lest
sub-openiieegDeerronn0s
openisag
4
Y
sub-openieeyDeetooiis.
openiceg
9
sub opernieggeeetooi0003
openierg
text
sulh reeeneeeeeeeee
openicey
12
M
sub-oponniceDeetrorfft
openiceg
28
M
train
uub-openicceDDetroittt
openieeg
sub Derieeeeeeeee
openieey
10
M
train
sub-oppenieeg
openiseg
16
M
test
sub- sub-operieeeDDettiittt
openieey
F
sub ogenieegDetrroi000.
openieeg
19
M
test
sul- oetrrppnnieegg/etonnnne777
oponiceg
12
F
sub-oponiiceeDeetrintttt
openiceg
44
M
Test
sub-openiceeDeetooitt
openicey
10
sub D0
openieey
15
M
train
sub-openieeg
openiceg
4
F
Y
train
openieey
10
F
train
suhh oetereeniieetttttinntttt
openiceg
12
train
wub-ogenieeeDDetronnnt.
openiceg
M
sub-opouliceeeetooittt
openiceg
14
M
train
openieey
suh openiiegeeeemiiii05
openicey
I
lest
sub- aub-openneeg
openieeg
M
train
openieey
M
sub- wuh-ogenieeeDeeoonnno
openiceg
14
train
sub-openicceDeeroortt.
openieeg
37
train
sub- sub-openicceDDemoitttt
openieeg
19
M
train
openieey
suh- Dertreeeeeeeeeeeeiiee.....
openiceg
14
F
train
sub-openiceg DetroitO6
openiseg
17
M
openicey
14
M
train
sub-openicegDettoittt
openicog
12
M
train
uub-openicce@eetooittt.
openiecg
sub oeneeeeeeeeee
openieey
27
F
test
De
openieey
10
M
sub-ogennieegDemmoi001
openiceg
19
F
train
sub-ogeniccgg::oooo022
openiseg
16
M
train
suih wth-rgeniieggDDeoonn77
openiceg
37
train
uub-openiceeDetoonntt.
openioog
13
M
test
sub-openicceeDeetoott77
openieeg
M
sub ogeriieggDeiivii007
openieey
8
M
train
suih auhh-gpenieegDetrriiiee Detroitt77
openiceg
15
M
sub-ogennieegDeemoinn7
openioog
15
F
N
train
sub-oponleegDeeooittt
openiseg
5
F
test
sub- uub-openieeg
openiceg
15
M
Y
Y
sub-openicceDeeroiittt
openioeg
4
M
N
train
sub-
aub--oeeniceeDeetoitttts
openieey
suh Deeiniegeeeeeiiii
openieey
18
IF
N
train
sub-rgennieegDerrrine
openiceg
sub-ooeenieegDeerrorttss
openicog
17
M
N
train
sub-openiieeDeetooitt6
openieeg
7
F
train
sub-openiieeDeerroittt
openiceg
14
M
sub-opennleeDDetootttt
openieeg
13
M
N
train
openieey
19
F
suh-
nuh-opennieegDeemii0990
openieeg
113
N
N
test
sub-ogonieeeDeroontty
openicag
17
M
Y
sub-opeuleegDetooit02
openiceg
M
Y
N
Y
N
train
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Patient Name
Dataset
Ago
Gonder
SO
SOZ
Res
Anat
Ey
Int
lct
Awk
Splin
suih 3
openiceg
41
F
Y
Y
N
Y
train
subb 4
openieeg
6
F
Y
Y
N
Y
N
train
suh- openieeeneegg 5
openiceg
12
openiceg
16
F
Y
Y
Y
N
Test
sub-oppenieegDetrooie
openiceg
M
Y
Y
train
sub-ogernieegDettviiigg
openieey
14
Y
Y
N
Irain
suih egennieegDDetrroii0
openiceg
13
F
Y
Y
train
uub-rgeniiee@Detroint000
openiceg
17
Y
N
Y
train
sub-oopenicceDDetoottt000
openiceg
5
F
Y
Y
Y
N
Y
N
Test
suh-ropenicegDeerroifff
openiceg
M
oubboopenicceeDetroitt11
openiceg
16
M
Y
Y
Y
N
sub-opeenieeeDDetoottt00
openieeg
15
F
Y
NNN
test
0gemieegDeettoil0005 suth
openieey
7
M
train
openiceg
14
Y
test
uub-openiceeDeetronttt
openiceg
sub-opeemiceeDDetoittt00
openiceg
16
train
sub-ogpenicegDeennoii
openiceg
13
M
Y
uub-opeenieepDetroiil 10
openiceg
11
F
Y
train
aubropenieeeDDetooill
openieeg
I0
F
train
openieeg/eeerooii 12
openieey
17
M
sulb
uub-ogenieeeDeerooi
openiceg
14
train
sub-openicce@eettoittt
openiecg
sub-ogeenieeeDDeeviiill..
openieeg
9
train
subboopeniceegDeerooii 16
openiseg
17
F
Y
train
sub-ooeenieegDeetoitt11
openicey
12
M
train
sub.
opennieeeDeettiiillll
openierg
11
M
text
sulh
ait-ropemireeg//etooiilll 19
openicey
12
M
train
sub-oopenicceDDetrritt222
openiceg
11
F
train
sub-opemiiceeDDeooott1121
openieeg
M
sub ubbrugeeniieee//ttilll2222
openieey
M
train
2
openiseg
13
M
test
sub-opernieegDeetool1222
openieey
F
train
sub
ogenieeegDDetooil112
openieeg
16
IF
train
sul- uuh-ogeenieegDDeroott2222
oponiceg
8
M
sub-ogpeniceeDeetoott222
openiceg
5
P
Test
aub-openiiceeDDeoooitt2
openieey
5
sub 2g
openieey
7
lest
openiceg
16
M
test
31
openieey
15
F
train
suhh
openiceg
R
M
test
3ub-ogemiiceeDetrrirt1333
oponiceg
14
F
Y
sub-oppeniceeDeetottt....
openiceg
5
F
train
sub- 3
openieey
13
M
sub aeh-opeeneeey//CLA00
openieey
20
M
train
sub-epenieegL
openieeg
12
M
train
sub 4pC.
openieey
19
train
sub-openiesgU:C1AA00
openiceg
14
F
test
sub-epenissgUCCAAG
sub-openiccg
openiecg
spenees
3
M
F
test
saaniety
M
suh aah-oppnnieel::11AAG0 A08
19
F
N
train
sub-epenisegUCLAA0
eoonicde
13
M
test
sub
aah-openieeyl::11A10
openiccy
F
sub-openie:gUUCAAI
oponiceg aonnico
F
train
sub-opemissgl CLA12
openiecy
F
sub-openieeg((CAAA
18
F
test
sub-epenneeyy.CLAAA.
15
F
sub-openieegUCCLAA5
openiceg gonncco
19
F
N
test
sub-@panissgUCLAA
openiseg
15
F
train
auh-openieegl:111A117
oponiceg
M
train
sub-oponissuUCCAAI
openicog
20
M
train
sub-epemisgl CLA19
openieeg
sub CCL2
openieey
12
lest
suh-openie:gl:C1..0
openiceg
M
sub-epenissgUCCLA22
openioog
22
F
N
train
Sub-epanissgUCCAA2
openiseg
20
F
test
sub-openiieggUCCLA224
openiceg
14
F
Y
train
sub-opanicslUCCLA25
openiocg
23
F
N
test
sub-openieggLCLAAG
openieey
M
sub
<penneeyy(.CCAA27
openieey
6
F
N
train
aub-openie:gU:C1AA.
openiceg
17
sub-epeniscgUCCLA22
openicog
13
M
N
train
sub-spemssgg
openieeg
9
F
test
sub-openisssUUCLA.
openiceg
13
M
gpenieg
3
F
N
train
Lub-openieey
19
M
Ahh-openieegL:(CIAA..
shanieeg
9
M
N
N
test
sub-openieggUCL.S
doonicdo
18
M
Sub-qponiogUUCLA336
openiceg
17
N
Y
N
test
32

---

Published as a conference paper at ICLR 2026
Patient Name
Dataset
Ago
Gonder
SO
SOZ
Res
Anat
Ey
Int
lct
Awk
Spli
suh-openieggU[CI
A37
openiceg
12
F
Y
Y
Y
train
sub
openieeg
7
M
Y
N
Y
N
lest
sub.openiceg!:00
openiceg
sub-opeenisggUCCLA440
openiceg
18
F
Y
Y
N
train
sub-openiseg
openiceg
16
F
Y
Y
train
sub-openieegglCLLM42
openieey
25
Y
Y
train
sub
openiceg
17
M
Y
Y
train
abb-openieegUCCLA44
openiceg
M
Y
Y
Y
Sub-openisseUCLLA45
openiceg
25
F
Y
N
Y
N
train
wth-openic:gl:C01A446
openiceg
NNN
ubb-oponissgU/CLA447
openioog
16
M
Y
N
test
sub=opemiegg
openiceg
12
M
train
sub openieyylCC.V4
openieey
28
M
sub-
whh-openiegg..T-AS A50
openiceg
2
M
train
sub-sourcesinjjh100
sourcesink
train
sub-sourcesinkkb105
sourcesink
train
sub-soureesinkNIH
sourcesink
57
Sub-sourcsinnKNHH00
sourcesink
25
M
train
sub -ob--eenniiiiiiiiii
sourcesink
27
M
train
sub sourcesinkNI112
sourcesink
31
M
sourcesink
36
F
train
39
M
sub wor.succcsssiiiiiee
sourcesink
41
M
train
sub-sourssiinkNIHG
sourcesink
P
train
uub-sourcesinkNIHH
sourcesink
46
M
train
sub swurcesinkkiin
sourcesink
37
M
text
sourcesinkNT199
sourcesink
16
sub-sourceeinkptt
sourcesink
30
F
test
sub-sourcesinkp22
sub souncesikkpp
sourcssink
45
M
train
Sub-soousssinnkPY1BS.
sourcesink
62
M
train
Sead-souuceeiikPPPPP 18N007
sourcesink
32
sub wwuncesin 18N001
sourcesink
24
train
sub- wot.sssuucceiikPPyyii 18N0S
train
sub-sourcesinkPPY wwuncesiikkkyy
cub-sourcesimkPP 19N011
sourcesink
48
M
test
sub wourcesinkPP 19N022
sourcesink
32
M
train
sub-souncesinkPPY 19N026
sourcesink
35
Y
train
souncesinkrnss00
sourcesink
36
wuh-sourceeinkmmm000
sourcesink
21
M
train
sub-sourcesinkms000
52
sub-soureesinkmsl005
sourcesink
23
M
test
and-souuccesinnmms0066
sub
sourcesinkmss009
sourcesink
48
M
test
lub-souncesink.mss011
sourcesink
24
F
train
sourcesinkrs0013
sourcesink
25
M
train
anh-sourcesinkmms0
sourcesink
36
M
test
1ub-sourcesinkmss055
sub-scurcesinkumf(u))
sourcesink
37
test
sourcesinkumfuug
39
sub-
sourcesinkumf000
sourcesink
43
M
test
aob-sourrssinuum0000
sourcesink
sub-
snarcesinkumm000
sourcesink
32
train
sub-zurich01
zurich
train
sub-zurich02
sub- zurich033
zurich
20
F
test
zurichhr
sub-zurich05
zurich
40
M
train
sub-zurich06
zurich
48
M
test
sub-zurich07
zurich
25
M
sub-zurich08
zurich
21
F
train
sub-zurich09
zurich
52
M
sub surich10
zurich
37
M
N
lest
suh-zurich11
zurich
zurich
40
16
M
Y
Y
train
sub-zurich13
zurich
17
M
Y
test
46
F
31
F
N
train
17
F
suh
10
MI
N
train
suh-zurich18
(0
M
38
M
N
sub-
17
M
Irain
33


Reviews:
[{"review_id": "O3VA6uaC2n", "reviewer": "Reviewer_5BzA", "summary": "The paper assembles a multi-center presurgical iEEG benchmark by merging several public cohorts, harmonizing metadata/recordings, and adding expert-validated event labels (spike-associated HFOs). It defines clinically motivated tasks (event classification; pathological channel/patient-level analyses tied to SOZ/resection/outcome), proposes subject-level splits, and reports baselines spanning biomarker-centric pipelines and long-context end-to-end models. The authors state that data, code, and checkpoints will be released.", "questions": "1. Provide a bullet list of post-merge artifacts created by the authors (new labels, unified ontologies, resection mask normalization, QC) versus fields inherited unchanged.\n\n2. Report leave-one-center-out and per-center results for all primary tasks, with thresholds fixed on training centers and calibration (e.g., reliability) reported.\n\n3. Add brief ablations for referencing, resampling rate, and artifact policy to show conclusions are not artifacts of these choices.\n\n4. Summarize the inter-rater protocol for event labels and how detector-seeded candidates avoid biasing the class distribution; include agreement statistics and adjudication steps.\n\n5. Share (or commit to camera-ready) the repository with preprocessing/evaluation code, exact split files (including center IDs), model checkpoints, and prediction files used to compute tables; include a datasheet/dataset card and a concrete data availability statement (host, license/access path, date).\n\n6. Either add a canonical EEG CNN (or justify its omission) and a simple linear baseline on robust features, or clearly explain why they are not applicable here.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Consolidates fragmented iEEG datasets into a unified benchmark with consistent structure and task definitions, which could materially improve comparability in the area.\n\n2. Tasks and evaluation targets are tied to familiar clinical surrogates, increasing practical relevance.\n\n3. Adds a sizable layer of expert-validated event annotations (spkHFOs) with a described protocol and agreement checks.\n\n4. Includes both biomarker-driven and long-context end-to-end baselines, highlighting trade-offs and opening room for future work.\n\n5. If released with strong artifacts (schema, loaders, splits, checkpoints), the resource can become a de-facto standard.", "weaknesses": "1. It’s hard to separate what is newly curated/validated post-merge (re-labeling, unified clinical ontology, normalized resection masks, QC decisions) from what is simply inherited. Please enumerate concrete new artifacts.\n\n2. Pooled or random subject splits are insufficient for a multi-center resource. The paper needs leave-one-center-out/per-center reporting for the primary tasks, not only a subset, to demonstrate robustness to site effects.\n\n3. “Harmonized” is described procedurally (e.g., resampling, montage, channel cleaning), but there’s little quantitative evidence that results are stable to these choices (referencing/resampling/artifact policy). Short ablations would increase trust.\n\n4. SOZ/resection/outcome fields appear inherited rather than re-adjudicated. Without normalization across centers, surrogates may encode site-specific conventions; sensitivity analyses to alternative definitions would help.\n\n5. The set omits a canonical EEG CNN (e.g., EEGNet/DeepConvNet-class) or a clear rationale for excluding it; a simple linear baseline on strong features would also anchor expectations.\n\n6. Code (preprocessing, splits, evaluation) and exact split files/checkpoints are not available during review; data access is deferred. For a benchmark paper, this materially limits verifiability", "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": "yjegATlENe", "reviewer": "Reviewer_Ew4m", "summary": "The core contribution of this paper is the construction of a large-scale, comprehensive iEEG dataset—Omni-iEEG, along with standardized benchmark tests for the dataset. The dataset includes patient data from multiple epilepsy centers and ensures data quality through expert annotations. The authors also conducted a series of machine learning model tests and evaluations on this dataset, particularly for clinical tasks such as epilepsy event classification and focal region identification.\n\n1. The paper seems to primarily focus on the construction of the dataset, while ICLR typically places more emphasis on algorithmic, model, or methodological innovations. How does this dataset significantly differ from existing EEG/SEEG datasets? If it is simply an extension of existing datasets, can the authors clarify its novelty more specifically?\n2. The paper mentions that the dataset will be made publicly available. Could the authors further clarify the dataset's release method and the specific platform for access?\n3. While the paper mentions the potential applications of the dataset, there is no concrete benchmark testing or application scenario presented. Can the authors provide one or two actual use cases or examples to show how this dataset can advance epilepsy detection and prediction models?\n4. The quality of the dataset is crucial for its application. While the paper mentions the annotation process and methods, it does not provide detailed information about the accuracy and consistency validation of these annotations. Could the authors elaborate on this aspect?\n5. Does the dataset include a diverse range of epilepsy patients to ensure its representativeness? Can the authors provide detailed statistics on the dataset, such as patient age, gender, and epilepsy type?\n6. Details regarding data preprocessing and collection methods could be further refined. Adding easy-to-understand flowcharts or tables would help readers better understand the dataset construction process.", "questions": "1. The paper seems to primarily focus on the construction of the dataset, while ICLR typically places more emphasis on algorithmic, model, or methodological innovations. How does this dataset significantly differ from existing EEG/SEEG datasets? If it is simply an extension of existing datasets, can the authors clarify its novelty more specifically?\n2. The paper mentions that the dataset will be made publicly available. Could the authors further clarify the dataset's release method and the specific platform for access?\n3. While the paper mentions the potential applications of the dataset, there is no concrete benchmark testing or application scenario presented. Can the authors provide one or two actual use cases or examples to show how this dataset can advance epilepsy detection and prediction models?\n4. The quality of the dataset is crucial for its application. While the paper mentions the annotation process and methods, it does not provide detailed information about the accuracy and consistency validation of these annotations. Could the authors elaborate on this aspect?\n5. Does the dataset include a diverse range of epilepsy patients to ensure its representativeness? Can the authors provide detailed statistics on the dataset, such as patient age, gender, and epilepsy type?\n6. Details regarding data preprocessing and collection methods could be further refined. Adding easy-to-understand flowcharts or tables would help readers better understand the dataset construction process.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1.The Omni-iEEG dataset contains data from multiple epilepsy centers, offering a large sample size and diversity, which better represents different populations and pathological types.\n2.The dataset's annotations are performed by experts, ensuring high quality and accuracy, making it suitable for machine learning model training and clinical applications.\n3.The dataset provides standardized benchmark tests, enabling other researchers to evaluate the dataset easily, promoting model and method comparisons and validations in future studies.\n4.The dataset has wide applications in epilepsy detection, prediction, and seizure foci identification, with great potential to advance research in related fields.\n5.The dataset will be made publicly available, promoting wide usage in both academia and industry, encouraging data sharing and collaboration.", "weaknesses": "1.The paper focuses mainly on dataset construction, while ICLR typically emphasizes innovation in algorithms, models, or methods. The dataset's contribution does not highlight any novelty in algorithms or methods, which may not meet ICLR's review standards.\n2.No concrete application scenarios presented: While the paper mentions the potential applications of the dataset, it does not demonstrate its actual effectiveness or value through specific benchmark tests or application scenarios, lacking real-world examples to support its claims.\n3.Insufficient detail on annotation accuracy and consistency: Although the paper mentions the annotation process, it does not provide a detailed description of how the accuracy and consistency of annotations are verified, leaving uncertainty about the reliability of the annotations.\n4.Lack of diversity and representativeness of the dataset: Although the dataset includes data from multiple centers, it does not adequately address whether it includes patients with various types of epilepsy. Detailed statistics about patients’ age, gender, epilepsy types, etc., are lacking.\n5.Insufficient detail on data preprocessing and collection methods: The paper does not provide enough detailed descriptions of the data preprocessing and collection methods, making it difficult for readers to fully understand the dataset construction process. There is a lack of clear flowcharts or tables to support this.\n6.Unclear data release plan: While the paper mentions that the dataset will be made publicly available, it does not specify the exact release methods and platforms, lacking transparency and a clear plan for making the dataset accessible.", "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": "Lctgm8T03z", "reviewer": "Reviewer_ngqJ", "summary": "The paper introduces Omni-iEEG, a large-scale dataset and benchmark for epilepsy research, comprising 302 patients and 178 hours of high-resolution intracranial EEG (iEEG) recordings from eight leading epilepsy centers. It addresses challenges in clinical EZ localization by providing harmonized data formats, metadata, and over 36K expert-validated annotations of pathological events (e.g., spkHFOs). The dataset supports reproducible research with standardized tasks—Pathological Event Classification and Pathological Brain Region Identification—plus exploratory tasks, demonstrating potential for end-to-end modeling and transfer learning from non-neurophysiological domains.", "questions": "1. Can the authors provide inter-rater reliability metrics (e.g., Cohen’s kappa) for the 36K annotations to ensure consistency?\n2. What criteria were used to select and tune the HFO detection algorithms (e.g., Navarrete et al., 2016), and how were artifacts filtered?\n3. Can baseline performance metrics (e.g., AUC, F1) be provided for the benchmark tasks on Omni-iEEG?\n4. How was transfer learning from non-neurophysiological domains tested, and what specific performance gains were observed?\n5. What additional privacy measures were implemented beyond de-identification, and how will annotation scalability be managed?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- **Originality**: First large-scale, multi-center iEEG dataset with harmonized annotations and clinical metadata.\n- **Quality**: Expert-validated data and diverse tasks ensure robustness and clinical relevance.\n- **Clarity**: Clear task definitions and dataset structure, supported by visuals.\n- **Significance**: Bridges ML and epilepsy research, enhancing reproducibility and translatability.", "weaknesses": "- **Methodological Flaws**: Inter-rater reliability for 36K annotations is not quantified, risking bias. HFO detection algorithm selection lacks justification.\n- **Experimental Gaps**: No baseline model performance or cross-validation results are provided for benchmark tasks. Transfer learning potential is theoretical without empirical support.\n- **Oversight**: Data privacy protocols beyond de-identification are unclear. Scalability of annotation processes for future expansions is unaddressed.\n- **Validation**: Claims of clinical translatability lack pilot study results or online validation.", "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)