ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Authors: Chenyu Liu
Yuqiu Deng
Tianyu Liu
Jinan Zhou
Xinliang Zhou
Ziyu Jia
Yi Ding
Venue: ICLR 2026 Poster (2026)
Keywords: Electroencephalography
In-context Learning
Large EEG Model
Primary area: applications to neuroscience & cognitive science

Abstract: Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) address this by pretraining encoder-centric architectures on large-scale unlabeled data to extract universal representations. While effective, these models lack decoders of comparable capacity, limiting the full utilization of the learned features.
To address this issue, we introduce ECHO, a novel decoder-centric LEM paradigm that reformulates EEG modeling as sequence-to-sequence learning. ECHO captures layered relationships among signals, labels, and tasks within sequence space, while incorporating discrete support samples to construct contextual cues. This design equips ECHO with in-context learning, enabling dynamic adaptation to heterogeneous tasks without parameter updates.
Extensive experiments across multiple datasets demonstrate that, even with basic model components, ECHO consistently outperforms state-of-the-art single-task LEMs in multi-task settings, showing superior generalization and adaptability.

Published as a conference paper at ICLR 2026
ЕCHO: TOWARD CONTEXTUAL SEQ2SEQ PARADIGMS IN LARGE EEG MODELS
Chenyu Liu', Yugiu Deng's : Liu2, Jinan Zhou?, Xinliang Zhou', Ziyu Jia'! Yi Ding't
College of Computing and Data Science, Nanyang Technological University, Singapore
Schooo of Mechanical Engineering, Xi'an an Jiaotong University, Xi'an, China
Nutani,, CA, USA
'Institute of Automation, Chinese Academy of Sciences, Beijing, China
chenyı003@ntu, edu, sg, genuineyukeol gmail com, iiutyestu.xjtu.... jinan. www.hnotani.....mm.......m com, xinliang001 entu.edu.se. sg, jia www.ouuoooooooo.oooooooooookoo.m ding. ying.yiee wet.tuuuuuuu.......
ABSTRACT
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) address this by pretraining encoder-centric architectures on large-scale unlabeled data to extract universal representations. While effective, these models lack decoders of comparable capacity, limiting the full utilization of the learned features. To address this issue, we introduce ECHO, a novel decoder- centric LEM paradigm that reformulates EEG modeling sequence-to-sequenee learning. ECHO captures layered relationships among signals, labels, and tasks within sequence space, while incorporating discrete support samples to construct contextual cues. This design equips ECHO with in-context learning, enabling dynamic adaptation heterogeneous tasks without parameter updates. Exten- sive experiments across multiple datasets demonstrate that, even with basic model components, ECHO consistently outperforms sate-of-the-asd art single-task LEMs multi-task settings, showing superior generalization and adaptability.
INTRODUCTION
Eleetroenceephalograpyy (EEG), owing its portability and cost-effectiveness, has become the most widely used neural recording modality. Leveraging these advantages, EEG has been broadly ap- plied to emotion recognition (Liu al., 2024b), motor imagery (Ding al., 2025), and diverse cognitive paradigms, which in turn demands models capable of maintaining generalization across heterogeneous tasks. Following the trend of large-scale models, researchers have proposed a se- ries of Large EEG Models (LEMs) that place pretrained encoder architecture at the core (Zhou et al., 2025a). These models are typically trained on large collections of unlabeled EEG data with self-supervised objectives such masked reconstruction (Jiang et al., 2024b) contrastive pre- diction (Wang et al., 2024a), thereby producing latent representations with strong generalization capacity that have demonstrated remarkable transferability across tasks.
Although these pretrained EEG encoders have been shown to learn high-quality representations, a major limitation remains: they lack decoders of comparable capacity transform such represen- tations into usable predictions. As illustrated in Figure (top), their decoding paradigm typically relies on lightweight classifier (orders of magnitude smaller than the pretrained encoder) com- bined with additional fine-tuning to adapt the representations for downstream tasks. In other words, the model's i success on downstream tasks largely depends on whether the encoder can "bend" its representations during fine-tuning to accommodate limited-capacity decoder.
Such adaptation small-scale downstream data inherently high-risk. On the one hand, the en- coder may sacrifice its pretrained general knowledge to meet the fine-tuning demands of the de- coder, leading to knowledge forgetting and degraded generalization (Guan al., 2025). On
"Equal contribution
1 "Correppnniing author

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Encoder-centric LEM Decode Paradigm
Classifier
Feature Space
Probability
Embbediin
Space
Fully Connect
77..2
Labe!
EEG
Label
Caaae Average Adtentiont High High-aamnn Gamman Antton
$5:52
81:39
LLM-centric LEM Decode Paradigm
LLM Decoder
Text Embedding Space
NOGGNE
EOHO Enable LEMs with
EEG Takens text Takens
Text
Self Attention
Label
<Labet>
EEG
In-context Learning Multi-Task Abilities
68.51
23.00
Decoder-centric LEM Decode Paradigms(ECHO)
p4.32
SEED
Disarder
Decoder
Sequence Space
24.34
AS
MBSH-SCIAR
Support EEG Tokeens
EEG Tokend
Support Takens
Support
65.65
ke
yest
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oets
<Tasko
KLabel
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Label
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<Label>
Mapping
Learned
Conteetnall Mapping
Protabiity
Signal
<Task>
<Label>>
ECHO
CodeBrain
CbraMod
Labram
ECHO
Figure 1: a. Top: Encoder-centric LEMs learn a direct mapping from EEG features to labels. Mid- dle: LLM-centric LEMs follow the same scheme but shift it into the text embedding space. Bottom: ECHO extends such mapping by modeling various mappings within the sequence space. b, Perfor- mance comparison. ECHO indicates ECHO that do not adopt decoder-centrie paradigm. Detailed experimental settings and results are provided in Section 4 and Table 2.
other hand, when the decoder itself insufficient to reliably extract fask-discriminative informa- tion, reliance on limited labeled data amplifies training uncertainty and makes the model sensitive to noise patterns (Hao et al., 2025). Consequently, current paradigm remains constrained by decoder bottlenecks, preventing LEMs from realizing their generalization potential.
While some recent studies have explored incorporating large language models (LLMs) as decoders, this paradigm remains fundamentally constrained: it does not move beyond the "EEG-to-label" mapping, but merely shifts it into the text embedding space. As illustrated in Figure a (middle), this approach requires the LEM encoder and the L.MM decoder to align by projecting EEG tokens and labels into shared text embedding space, where the mapping is performed under the constraints of textual prompts (c.g., restricting the label types (Jiang et al., 2024a)).
The inductive biases of language models cannot be reliably transferred to time series BEG task (Tan et al., 2024). This stems from fundamental structural differences between EEG and language or vision. EEG relies the precise localization of critical temporal dynamics, which are inherently misaligned with the static semantic patterns of text or images (Jing et al., 2024; Queen et al., 2023). As result, projecting EEG directly into the text embedding space often drives models to exploit perficial correlations, such as mapping noise patterns to semantic labels (Wang & Ma, 2025), while diluting or even corrupting task-relevant information in the shared space (Almudévar et al., 2025). Ultimately, text fails to serve as a genuine semantic bridge across modalities and instead func- tions merely as surrogate label space, leaving LEMs without the reasoning and in-context learning (ICL) capabilities expected from LLMs.
To overcome the limitations of existing paradigms, we propose decoder-centric sequence-to- sequence (Seq2Seq) approach that enables LEMs to jointly model multi-task EEG representations while leveraging discrete samples as contextual support. As illustrated in Figure (bottom), the input is structured as sequence comprising target EEG samples together with supporting EEG instances and their associated task and label tokens. The model performs next-token prediction, establishing associations between support samples and the target based on their mapped relation- ships. This process guides the generation of an output sequence that integrates both label and task tokens, thereby achieving multi-task learning within unified framework. In summary, refer to this new paradigm as ECHO: a decoder-centrie framework and sequence-based learning method that preserves task discrmnnciee capacity while equipping LEMs with ICI.
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To validate the effectiveness of our approach, adopt off-the-shelf model components to avoid conflating our paradigm with architectural enhancements. We conduct extensive experiments across multiple EEG datasets, showing that ECHO consistently outperforms the latest single-task LEM baselines, even in multi-task settings (see Figure b). ECHO can infer both the target task and its specific paradigms (e.g., identifying motor imagery and distinguishing its variants) without plicit prompts. Moreover, ECHO demonstrates ICL ability, adapting to new tasks and environments under the guidance of support samples. These results highlight the critical role of ECHO in ad- vancing cross-task generalization and complex scenario modeling, while also providing insights for unlocking the full potential of existing LEMs.
PRELIMINARY
2.1 MULTI-TASK LEARNING FOR LEMS
Given heterogeneous EEG datasets, each dataset is represented as D (X.Y,t). where X E NXXXCC denotes the EEG imputs with N samples, each represented by T time steps and C chan- denotes the corresponding dataset-specific labels, where [Vaa depends on the
nels; Y E RRNxpaa dataset; and t is a task identifier specifying the experimental paradigm. The objective of LEMs is to learn generalizable representations, while performing conditional mappings f(X - Y. Based on this definition, the proposed decoder-centrie paradigm differs from the two existing ones.
Encoder-centric LEMs: The conditional mapping from EEG task specifie label modeled as:
f(x t) (() + Y,
(1)
where Eo,( (.;02) and Co. (:; (4) denote the encoder and classifier with parameters Od, da fine-tuned on dataset 'D. Consequently, this paradigm fails to generalize across datasets.
LLM-centric LEMs: The mapping incorporates both EEG and auxiliary textual prompts, with the decoder instantiated as an LLM:
f(x (t) DILM (E(X), <|tet <1yl1>
where E(X) encodes the EEG tokens, < text >> denotes textual tokens, and DLLM(.) is the LLM decoder that operates in the text embedding space. The output 
 |> is textual label. Thus, the key distinction from the encoder-centric paradigm lies in shifting the mapping into the text embedding space.
The proposed paradigm represents both inputs and outputs as structured sequences, guiding LEMs to perform multi-task EEG learning and contextual modeling within unified decoding framework:
Decoder-centric LEMss:
{(X), suppport }>}; Sout {< suppupt < <ly 1>, speciall |>},
S = {<1special)
(3)
f(x D( Sm) Sout
(4)
where < </speciall denotes special tokens, such start delimiter symbols, which provide structural cues for sequence decoding. {8(X,)) and E(X) represent the collection of support EEG tokens and the target EEG token. <I support >> refers task and label tokens for support samples. Through next-token prediction, D(-) infers the task token < <ltask| and label token <Iyl >> of the target sample by leveraging the mapping relationships established from the support samples. Therefore, under this Seq2Seq learning scheme, the decoder is required to learn mappings beyond label prediction (see Section 3.2 for details).
2.2 TECUNICAL CHALLENGES
While decoder-centric LEMs hold promise for advancing framework for LEMs, their imple- mentation introduces three key technical challenges. We detail these challenges below and present the corresponding technical contributions in Section 3.
C1: Inconsistency of EEG channels. The number of EEG channels C and their ordering T(C) are not standardized across datasets, posing significant challenges for generalization. Existing LEMSs
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attempt to mitigate sensitivity to channel order during training through positional encoding strategies (e.g., asymmetric conditional positional encoding (Wang et al., 2024b)). However, at inference, the model still requires channel configurations to exactly match those seen during training. In multi- dataset settings, particularly in cross-dataset scenarios, encountering unseen channel arrangements disrupts spatial alignment and substantially degrades performance. To address this issue, adopt a channel alignment preprocessing strategy (see Section 3.1).
C2: Heterogeneity of sequence components. The input and output sequences often consist of heterogeneous tokens, which creates difficulties for modeling. EEG requires capturing fine-grained temporal evolution, while discrete symbols encode semantic or task-control logic. Directly mixing these heterogeneous elements makes it difficult to balance continuous and discrete information. Furthermore, EEG samples may serve different functional roles (e.g., context vs. prediction targets), which the model must distinguish despite their homogeneous form. To address this, we propose hybrid positional encoding mechanism (see Section 3.2).
C3: Absence of symbolic structure in EEG. Unlike language, EEG lacks discrete symbolic struc- ture, making difficult for LEMs to acquire ICL naturally. In language models, autoregressive pretraining over diverse discrete contexts (e.g.. documents, dialogues) enables next-token prediction to act as implicit function fitting, allowing examples to be reused at inference. EEG models, how- are trained on continuous temporal dynamics that require strict temporal coherence, preventing flexible context transfer across tasks or samples. To address this, we propose a Seq2Seq in-context training approach (see Section 3.3).
METHOD
In this section, we present the methodology of decoder-centric LEMs and the technical contributions that address the aforementioned challenges. Section 3.1 introduces the overall architecture design of ECHO. Section 3.2 explains how ECHO operates under the Seq2Seq formulation. Section 3.3 de- scribes the training objectives and optimization strategies that endow LEMs with ICI. Each section begins with Intuition subsection that outlines the rationale behind the technical design.
3.1 MODEL ARCHITECTURE
The design of ECHO follows core principle that employs simple and established archi- tectural components highlight the impact of the paradigm shift itself.
Intuition:
(a) For channel unification, we intentionally adopt a straightforward yet general preprocessing block to avoid confounding the core contribution of ECHŌ (stronger alternatives, such as edge- learning strategies used in GNN-based EEG models (Liu et al., 2024a), can replace it). Because EEG acquisition adheres standardized electrode systems with recorded channel information, channels can be normalized at the preprocessing stage (Figure 2 (left)).
(b) For the model components, we build on established networks: the encoder block (Figure 2 (middle)) is simplified deep ConvNet (Schirrmeister et al., 2017) with tokenizer, and the de- coder block (Figure (right)) is transformer decoder with pre-activation residual blocks (Child et al., 2019). This emphasizes that ECHO is not bound specific modules but represents paradigm that can flexibly integrate stronger architectures to further improve performance.
To resolve C1, we establish standardized template channel set based on prior neuroscience knowledge, where the number of channels C and their ordering π (C) are predefined and fixed. For each standardized channel ट, we define mapping set Mz. which contains all possible aliases or adjacent variants of that channel under different electrode systems (the template set is provided in Section 4.1). Given EEG collection X with channel number C and corresponding channel names, we map each channel name in X to the appropriate Mr following the ordering (C), yielding the matched channel subset X. X. Then, the alignment process is computed as:
Preprocess Block:
X
Σ x BE TC)
E NxTxx
(5)
X
+1
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BEG Patirets with Charadl Setting
Enable In-context Learnin& Multi Task Abiiiless
ECHO
T; Postive
Tis
Tine
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Nadral
Megatio
EOT
Next Token Prediction
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Encoder Block
[(Xg]
Decoder Block
Self Alf--tttetion
Fully Connect
Pestfooel
Differend Electrode System
(PEpp)
Posiional Eneraing
Widdou-leee
(PES
Deznding
Cross-Attents Attention
Self Deff-Attentio
Channel Alignment
Global Avg
Stardard System
Fully Connect
Spatial Conv
Cross-Ares Attention
Temporal Conv
5(X)
Took
O 1YPa
Self AtleAttteton
Sliding Windows
Serele
Sapport Sampliss
Positianal
(PE
T
Tix
2013
EEG Datacets with Unditd Charnel Setting
SOT
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T
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Classifler
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Z
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SOT
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Suipport
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Decoder
Training Phase
Encoder
Decoder
Hepping
rhecko
happyn
etsait
Hepphn
ESED Hore
Randew Support
Encoder
Decoder
Special
Tokan
Taken
Leareed
Contestrua
Tolens
Untrwes
Freaze
Figure 2: a, Overview of the ECHO framework. From left to right: preprocessing, encoder, and decoder blocks. The inputs and outputs are shown at the top-left, bottom-right, and top-right corners, respectively. b. Four sequential learning steps within the sequence format enable ECHO to capture diverse mapping relations. Multi-stage training strategy of ECHO.
where X denotes the aligned EEG with standardized channel configuration. The normalization term XXe + ensures stable averaging. If Xa] O, the channel is padded with zero.
Encoder Block: The encoder block is designed to transform preprocessed EEG signals X
N into tokens. First, X is segmented along the temporal dimension using sliding win- dows of length L and stride S, yielding a collection of K = (T L)/S + segments denoted {
Each segment is processed by a convolutional head F (-) based on the deep ConvNet and tokenized into a sequence of vectors:
... 2 [K). .............
E(X) [h1, h2. hkk T T(th))
T TU F ({xx)))
(6)
1=1
where E(X) represents the sample-level EEG tokens produced by the encoder. T(-) represents the tokenization process (including self-attention), which flattens and projects segment features into window-level tokens and arranges them into sequence. Notably, since the tokenizer operates along the window dimension, we use hę denote the window-level tokens and segment features.
Decoder Block: The decoder D(-) adopts standard transformer decoder architecture, where self- attention models the dependencies within the textual sequence and cross-attention enables interac- tion with {(X):
Sau D(S,a))
3.2 SEQ2SEQ FORMULATION
Intuition: The Seq2Seq formulation guides ECHO to perform progressive learning through fixed serialization scheme.
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(a) A fixed sequence corresponds to a consistent "problem-solving strategy." As illustrated in Figure 2 b, ① the support EEG samples and their tokens serve as worked examples, enabling ECHO to learn mappings between EEG, task and label tokens; 2 the model then generalizes these mappings from examples to the target sample; @ ECHO then conducts stepwise reasoning by first predicting the task token and subsequently deriving the label token conditioned on both the task and EEG tokens; @ finally, by predicting the end-of-task (EOT) token, ECHO learns to recognize task termination. Through this unified sequence, ECHO acquires both in-context and multi-task learning capabilities, allowing it to autonomously select the most compatible label token from all known labels without requiring an explicit task specification.
(b) To prevent confusion among heterogeneous components, ECHO employs three-par part posi- tional encoding strategy. The first models the temporal structure within each EEG sample, al- lowing the model to capture the sequential dynamics of neural activity. The second distinguishes support from target samples, clarifying their functional roles in the sequence. The third encodes the semantics of textual markers such task tokens, label tokens, and the EOT token. Together, these positional cues enable the model to jointly handle the continuous dynamics of EEG and the discrete logic of tasks within single serialized space.
Sequence Format:
During training, the input sequence starts with < SOTT >; followed by multi- ple < support >> entries that encode the task and label tokens the support EEG samples. The < <|task >> token then specifies the paradigm of the target EEG (e.g.. < <MII| >>, EMO | |>), and <Iy denotes its ground-truth label. The output sequence mirrors this structure but ends with EOT to mark completion. During inference, the model receives SOT >> followed by the target EEG tokens (optionally with support samples). ECHO then generates only the task token and the predicted label token, and terminates with < EOT
Hybrid Positional Encoding: To solve C2, we propose a hybrid positional encoding. Given an
EEG sample Xn. the window-level tokens (h(,.1), 1) 22..)...... (,,,,,,,,)) k(,,k) obtained along the temporal dimension are encoded with learnable window-level position encoding:
K U
{{ (((.t)) + PE(m..) } k=1
(8)
where (E(n,K) denotes the learnable positional encoding assigned to the k-th segment within the EEG sample, As for the decoder input, given a hybrid EEG sample set (E(X,)) consisting of the target sample E(X) and support samples {Ex)) (= distinct learnable sample-level posi- tional encoding is assigned and uniformly added to all tokens within the same sample:
S+1 U
( + },
where PE denotes the sample-level positional encoding shared across all tokens of the n-th EEG sample. For the sequence of textual tokens 12543 are applied:
{ standard learnable positional encodings
(PE 2843
28+3
U (T + PE
(10)
3.3 IN-CONTEXT TRAINING
Intuition: Unlike large language models, where ICI. often emerges implicitly, LEMS require explicit guidance to acquire this capability. Thus, ECHO is trained with autoregressive next-token prediction under a multi-stage strategy: first, the encoder is initialized to accelerate convergence and yield usable EEG representations; then, the decoder is trained with progressively larger and more diverse support sets to develop multi-task classification and contextual learning capabilities.
Training Strategy: To address C3, ECHO is trained in two phases. As shown in Figure 2c, the Warm-up Phase initializes the encoder by pairing it with a unified classifier across all datasets to ob- tain stable representations. The Contextual Training Phase then follows, consisting of two rounds:

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the first uses fixed number of support samples to stabilize decoder training, and the second ran- domizes the support size to expose the model to diverse contexts. In both rounds, training starts with the encoder frozen and later jointly optimized with the decoder.
Next-token prediction: During training, given sequence of textual tokens (Ta InJ=l1 and EEG sample set {E(Xu)}, the decoder D generates the output sequence step by step. The conditional probability for the i-th output token is modeled
p(s | 8<i, (TT, )2=13 {E(X = D(s (T, 22 (E(X)))),
(11)
where 8; E Su denotes the i-th token to be predicted, and sci represents the prefix subsequence of previously generated tokens {s1, $2,..., 8.-1). The training objective minimizes the cross-entropy loss between predicted distributions and ground-truth tokens.
4 EXPERIMENT
4.1 EXPERIMENT SETUP
Dataset Setting: ECHO was trained on 12 publicly available EEG datasets spanning six task cat- egories and 26 classes.
(1) All datasets underwent a unified preprocessing pipeline appeline-band--aaa filtering, downsampling to 250 Hz, task-specific segmentation, and padding to 10s. (2) Heteroge- neous electrode layouts were aligned to standardized 75-channel system (see Section 3.1). (3) A consistent cross-subject split was applied across all experiments and baselines to ensure fair and gen- cralizable evaluation. Table summarizes the splits, task types, and input formats for the datasets reported in the main text (excluding BCIC 2020-T1 from training), with full details in Appendix A.1.
Table 1: Dataset Configurations
Dataset
Experimental Paradigms
Truin Indices
Validation Indices
Test Indices
Shope
BCIC (llcunner 2008)
Multi Mosor Entegery
5.6
chantels 4s
Bigh-GGamma (Schimmeister et il,, 2017)
Motre Imagery for Decoding
0-7
8-10
11-13
125 chaznels x 4
RCIC 2000-T1 (loong et al,, 20223
Hanl Imugary
0-9
10-14
15-19
chumncs 4s
SREDIIV (Zheng 2019)
Film-inucced Discrete Emnrtio Classifcation
0-9
10-11
12-14
channels 4s
SLED (Zheng & Lo, 2015)
(ilm induced Emotional Valence Classifiation
0.9
10-11
12 14
62 channels 4s
202ger2021 LR (Stages 2021)
Continooss ID Curse Custre (Lateral)
0-39
40-48
chunne's
Mumtaz2016 (Munttur, 2016)
Mujor Depicoive Diserder Desection
0-43
43-52
53-62
19 chumnas x5s
Memal Arithmeti (Zyma 20199
Worklod Assesment
0-25
26-30
chunne's 5s
Aticntion (Stin et al., 2015)
Disuiininniisudlleiii Resperree
0-15
16-20
21-25
30 dhumnss 4s
Baseline Selection: To evaluate ECHO, we compared it with six representative baselines covering diverse representation learning paradigms. EEGNet (Lawhern et al., 2018) employs convolution to extract features from raw signals; BIOT (Yang et al., 2023) uses block-based continuous tokeniza- tion; and LaBraM (Jiang et al., 2024b) combines masked reconstruction with vector quantization. EEGPT (Wang al., 2024a) and CBraMod (Wang et al., 2024b) emphasize masked reconstruc- tion of raw signals, while Code (Ma et al., 2025) learns by predicting discrete time-frequency tokens. Full baseline details are in Appendix A.2.
Model Setting: After preprocessing, EEG signals were segmented with a sliding window (length 100, stride 90). The encoder comprises 4 convolutional layers and tokenizer based on multi-head self-attention (8 heads, 4 layers, token dim 256). The decoder adopts a 4-layer Transformer with hidden size 384, 6 heads, and feed-forwwrd dim 1536. Full model details are in Appendix A.3.
Training & Environment Setting:
All training was conducted on 8XNVIDIA A 100 (40GB) GPUs. The process had two stages. In the warm-up phase, the encoder was trained for 90 epochs (batch size 64) using Adam with an initial learning rate of 5 x 10-5, mosine-decayed to 1 x 10 6 and dropout 0.2 to stabilize EEG feature extraction. In the contextual training phase, the full model was trained for 40 epochs (batch size 48, dropout 0. with differential learning rates (5 x 10-5 for the decoder, 5 x 10-6 for the encoder). Training followed a two-round schedule: 10 epochs with fixed 8-shot configuration, followed by randomized support counts (0-12) to expose the model to varied contexts and improve ICL robustness. Additional details are provided in Appendix A.4.
Evaluation Metries:
We adopt four standard evaluation metrics: Balanced Accuracy, Cohen's 's Kappa, Weighted Fl score, and the Area Under the ROC and Precision-
Curves (AUROC
and AUC-PR). Model selection is based on AUROC performance on the validation set. All experi- ments and baselines are conducted with five fixed random seeds 0, 1, 2, 3, 4, and we report the mean and standard deviation across runs.
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Table 2: Comparison results of different methods on downstream tasks.
Stieger202I-L.R
Methods
ACC-R
ROC ALC
PR AUCC
ACC-B
ROC AUC
PR AUC
EBGNe
07435 0.0015
0,8031 0.0235
0,8731
0:0331
0.8051 0.0124
0,8839 0.0123
0.8565 0.0078
BIOT
0.7234 e 0.0215
0.8212 +172200000 0.0049
0.8043 0.0143+0.016
0.7753 + 0.0052
0.8747 = 0.0054
0.8347 = 0.0054
EEGPT
0.7085 a 0.0350
0.8450 0.0241
0.8244 0.0194
0,7943 1: 1.0003
0,8968 2 0.0057
0.8354 a 0.0042
LaHraM
0.6851 0.041
0,7952 0.0241
0,8021 0.0136
0.8170 e 0:0037
0,9024 = 0.0015
0 0.0029
CBraMed
0,7262 0.0235
0,559
0.8400 0:0252
0.8424 0.0044
0,3399 0.0026
0.9297 0,0000
CodeBrain
0,7836 a: 0.0341
0,8755 0.0248
0,8543 0:0253
0.8126 2: 0.0037
0.9123 a 0.0024
0 a 0.0031
ECHO
0.6548 =: 0.0272
0.7493 = 0.0451
0,7592 0:0578
0.6123 = 0.0242
0 = 0.0206
0.9048 = 0.0217
ECHO (No Suppet)
0.7407 0.0047
0.8488 0.0100
0,8522 0.0103
0.8415 0.0031
0,9245 + 0.0021
0.9343
ECHO
0.5193 ; 0.0025
0.9020 0.0044
0.8962 0:0020
0.8534 0.0014
0,934 0.0001
0,933 0.0016
Mumiaz2016
High-Gamma
Methods
ACC-B
ROC AUC
AUC
ACC-B
ROC AUC
AUC
EBONN
09113 0.0004
0,9512 0.0006
0,9632 0.0045
0.8911 0.0412
0,9002 0.0091
BIOT
0,8789 0.0090
0,9664 0.0136
0,9764 0.0083
0.7343 0.0611
0,7931 0.0372
0,8198 0.0274
ERGIPT
0.8475 + 0.0233
0,9669 0.0069
0.9695 + D:0076
0,7161 + 0.0481
0.8266 + 0.0785
0.8349 + 0.0632
LaBraM
0,8986 :: 0.0028
0.9754 a 0.0050
0.9791 0.0041
0,7429 0.0386
0,8516 : 0.0255
0.8454 a 0.0246
CBraMod
0.8916 0.0017
1.0017
0,9800 0.0045
0,9765 0:006]
a: 0:0016
0.8335
CodeRmin
0.9012 0.0021
0,9729 + 0.0037
0,9721 0:0078
0.8138 0.0217
0,8421 + 0.0795
0.860 0.0102
ECHO
0.9698 :: 0.0012
0,9953 a 0.0023
0,9962 a 0:0015
0,8039 : 0.0436
0,8900 e 0.0279
0.8889 0.0314
ECIO (No Support)
0,9056 1 0.0211
0.9745 0.0100
0,9748 à 0.0058
0,8138 1 1.0023
0,9125 1 0.0016
0.9017 a 0.004
BCHO
0.8552 0.0031
0
0 0.044
Mental Arithmetle
Attention
Methods
ACC-R
ROC
PR AUC
ACC-R
ROC ALIC
PR AICC
EEGNe
0.5138 0.0411
0,5395 0.0109
0.5802 0.0441
0,6004 0.0123
0.6647 = 0.0180
0.6294
BIOT
0.5281 0000844
0,5970 0.048
0.5567 0:0249
0.6111 0.0411
0.7367 0.0162
0.7273 0.0132
LEGPT
0,5117 a: 0:0017
0,5612 a 0.0198
0,5041 0,0384
0.6674 1 0.0566
0.8015 a 0.0372
0,8103 a 0.0000
LaHraM
0.5993 e 0.0031
0,5700 = 0.0232
0.6341 a 0:0422
0.6785 = +.0223
0.7838 = 0.0302
0.7994 a 0.0198
CBraMod
0
0.7047 0:0428
0.6478 0.0258
0,7417 + 0.0175
0. 0.7468 0.0198
CodeBrain
0.6318 0.0845
0.6472 0.006
0,7412 0,0451
0.6215 0.0358
0.6311 0.0281
0,7029 0.0349
ECHOF
0.6008 =: 0.0343
0,6555 = 0.0281
0.6416 0:0337
0,7422 # 0.0346
0,7949 = 0.0623
0.8021 = 0.0278
ECHO (No Support)
0.5442 = 0.0023
0.68% 0.0036
0.6897 + 0:0042
0,8056 + 0.0021
0,8855 = 0.0029
0.8955 + 0.0034
ECHO
0.6851
0.7500 0.0062
0:0015
0.0009
=
0.8952
Bold indicates the penformance. Cyan highlight ECIO
Task Setting: All baselines are evaluated under single-task setting, where each dataset is fine- tuned separately and tested on its own test set. In contrast, ECHO is evaluated under a strict multi- task setting: trained once across all datasets without task-specific fine-tuning and directly tested on all test sets in a single pass. For ICL., 20 instances per subject are randomly sampled from each standardized test set as fixed context and excluded from evaluation. ECHO is given 8 support samples, but no task tokens, and must autonomously infer both the task paradigm and subcategories. The only exception is the Mumtaz2016 dataset, where each subject has a single label; evaluation for it is performed without supports.
4.2 EXPERIMENT RESULT
As shown in Table 2, six representative downstream datasets (different tasks) under a unified exper- imental setup (differing only in single-task vs. multi-task training) are reported. Complete results are reported in Appendix B. ECHO denotes the ECHO encoder trained without the Seq2Seq paradigm, while ECHO (No Support) refers to the ECHO model evaluated without any support- sample prompts. The advantages and corresponding limitations of ECHO are as follows:
(a) Comparison between ECHO and Baselines:
ECHO exhibits strong generalization capability. On cognitive tasks (SEED, Stieger2021-LRR Mental Arithmetic, and Attention), ECHO achieves an average improvement of +0.0602 in Bal- anced Accuracy, +0.0566 in ROC AUC, and +0.0316 in PR AUC over the strongest baseline. On clinical diagnostic tasks (Mumtaz2016 and High-Gamma), the best-performing ECHO variant shows an average gain of +0.0409 in Balanced Accuracy, +0.0225 in ROC AUC, and +0.0142 in PR AUC over the strongest baseline. ECHO also demonstrates unique capability: even with- out any external prompts, it can autonomously identify the corresponding task and its specific paradigm solely from the EEG sample itself.
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ECHO does not surpass SOTA several tasks. On tasks such Stieger2021-UD, BCIC-IV. 2a, and SEED-IV, ECHO remains noticeably below encoder-centric models that are explicitly op- timized for those domains. This highlights limitation in ECHO's
pnnlliizattoo-sspiiizzittt trade-off: while the framework aims for unified cross-task reasoning, its lightweight encoder de- sign is insufficient to model all domain-specific structures required by certain specialized EEG tasks. Detailed results and analysis are provided in Appendix B.I.
(b) Comparison between ECHO and ECHO
Seq2Seq paradigm improves performance. ECHO outperforms ECHO on most datasets, indi- cating that the full Seq2Seq paradigm is primary contributor to ECHO's gains. An exception is Mumtaz2016, where ECHO achieves the best performance (Table 2). Removing the sequential structure often leads to substantial drops (e.g., SEED ACC-B: 0.8193 - 0.6548).
ECHO's performance ceiling is constrained by encoder quality. While Seq2Seq offers consis- tent gains, their impact is limited when the encoder cannot model the dataset's structure, as seen in TUEV and PhysioNet. In these cases, ICL can provide improvements but cannot overcome the encoder's representational shortcomings or match encoder-centric SOTA models. Full results are in Appendix B.1.
(c) Comparison between ECHO and No Support:
ICL provides additional gains. ECHO surpasses its No-Support setting across all benchmarks, confirming the effectiveness of in-context learning. Without support samples, performance de- creases notably (e.g., SEED ACC-B: 0.8193 + 0.7407).
The effectiveness of ICL depends strongly on the distributional stability of support samples. This works well in datasets with clear structure, such as High-Gamma and SEED. In most EEG datasets, substantial cross-subject variability and noise make support unstable; few support sam- ples provide insufficient signal, whereas many support samples introduce accumulated noise and distribution shifts that reduce performance. Full results are in Appendix D.2.2.
4.3 ABLATION STUDY
We conducted ablation experiments to evaluate the two additional positional encodings introduced in ECHO. As shown in Figure 3, removing either encoding makes the model ineffective. (1) Remov- ing the sample-level positional encodings caused performance to drop to chance level, as the model could no longer distinguish boundaries between EEG samples and instead treated them as contin- uous sequence. (2) Removing the decoder textual positional encodings led to complete structural collapse, with the model producing disordered symbol sequences and no valid predictions. These results highlight that both encodings are indispensable: the former ensures functional separation of EEG samples, while the latter preserves syntactic and semantic coherence decoding.
A40
0:0
chance lsel
8440
dhenes love
chance love
livel
chance
0.00
M/A
0.00
0:00
0.00
N/A
6.00
0.00
N/A.
Balencel Aoweess
0.00
AAD
0.00
0.10
0.50
love
AUC
shancs
PRAA0 AMC
0.60
84p-
040
Belencal
0.00
RDC
040
0.40
chance lsel
0.00
0.00
0.00
0.00
MSSS4N0
Figure 3: The result of the ablation study for positional encoding
4.4 ZERO-So SHOT EVALUATION
As shown in Table 3, we evaluate ECHO's zero-shot generalization on two unseen datasets: Cho2017 and BCIC 2020-T1. On BCIC 2020-T1, ECHO exhibits strong cross-dataset transfer, applying the mapping strategies learned during multi-task training even without task prompts, while support samples further enhance performance. On Cho2017, ECHO again surpasses the encoder- only baseline, indicating that the Seq2Seq formulation provides stable adaptation to new acquisition settings and task paradigms. Additional generalization experiments are provided in Appendix B.3

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Table 3: Result of Zero-Shot Experiment
Cho2017 ECHO)
BCIC 3020-T1 ECHO)
Methods
ROC AUC
PR AUC
ACC-B
ROC
CBraMudi
0.7033 0.0327
0,8081 a 0.0429
0,7959 a 0.0230
0,5700 a 0.0185
0.6058 a 0.0236
0,5371 a 0.0186
ECHO
0.5610 0.0315
0.6021 a 0.0198
0,6126 1 0.0207
0.5250 A 0.0751
0.6796 1 0.0146
0.6697 1 0.0602
ECHO(No Support)
0.0023
0.0035
0.6267 0.0021
0.7500 a 0.0024
0.8232 : 0.0053
0.8153
ECHO
0,6044 0.0052
0.6470 0.0053
0.6460 0.0019
0.7782 = 0.0042
0.8566 0.0028
0.8552 0.0032
Note: Bold indicutes the best performance. Cyan highlight marks ECHO.
4.5 EXTRA EXPERIMENT
To evaluate ECHO under more diverse and clinically relevant conditions, we introduce three spe- cialized variants in the appendíx. The long-sequence variant in Appendix D.1.1 (ECHO*) shows the Seq2Seq + ICL framework remains effective under unified 30-second windows. The motor- imagery variant in Appendix D.1.2 (ECHO-MZ) indicates strong cross-dataset transfer across MI datasets. The ppilepsy-augmente variant in Appendix D.1.3 (ECHO FP) further demonstrates that support examples substantially improve seizure detection, highlighting ICL's ability to compensate for limited seizure-specific capacity.
We include additional diagnostic studies in the appendix to validate ECHO's design choices and framework-leve' contributions. Appendix D.2.1 analyzes channel-fusion and shows simple aver- aging is sufficient across heterogeneous layouts. Appendix D.2.2 studies support-size sensitivity and finds performance typically peaks at moderate support sizes. Appendix D.2.3 confirms the ex- tensibility of the Seq2Seq paradigm, while Appendix D.2.4 shows more stable generalization than standard multi-task learning with fewer parameters. Overall, these results support that ECHO's 's gains stem from the Seq2Seq + ICL framework rather than encoder capacity or task-specific shortcuts.
5 RELATED WORK
In recent years, research on large models for neural signals has increasingly centered on rep- resentation learning, with the primary goal of extracting robust and generalized representations. Existing approaches can be broadly categorized into two directions: reconstruction-bast and contrastive-based representation learning. In reconstruction-basee methods, representations are learned by recovering missing or future signals through masking or autoregression. For example, frequency-domain masking has been applied enforce
consistency in repre- sentations (Wang et al., 2023), while spatiotemporal joint masking has been introduced to model dependencies across both temporal and spatial domains (Dong et al., 2024). Autoregressive frame- works further extend this idea by predicting future signal segments, enabling representations that capture long-range dynamics (Caro et al., 2023). In contrastive-based methods, representations are improved by constructing positive and negative pairs to enhance robustness and discriminabil- ity. Temporal perturbations and frequency shifts have been used to ensure consistent representations across augmented views (Cai et al., 2023), while cross contrastive learning has been explored to expand the representational space, such as aligning EEG with text to ground neural signals in richer semantic domains (Jiang al., 2024a).
CONCLUSION
In this work, introduced ECHO, decoder-centric paradigm for LEMs, designed to highlight the untapped potential of decoders in EEG representation learning and task modeling. ECHO adopts a Seq2Seq formulation that jointly models the hierarchical relationships among signals, labels, and tasks within a unified sequence space, while leveraging discrete support samples to enable ICL. This design allows the model to dynamically adapt to diverse tasks without parameter updates. Extensive experiments on multiple public EEG datasets demonstrate that, even with basic architectural com- ponents. ECHO consistently outperforms state-of-the-a single-task LEMs in multi task settings, and further exhibits generalization in zero-shot and cross-dataset evaluations. Overall, these results show that ECHO provides viable pathway to overcoming the decoder bottleneck in existing LEMs.
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ETHICS STATEMENT
All datasets utilized this study are publicly available and distributed under appropriate usage li- censes. The data processed and presented exclusively in aggregated, privacy-preeerving formats, ensuring that no personally identifiable information is disclosed. We have thoroughly reviewed the possible ethical implications of this research and foresee risks adverse outcomes associated with its application.
REPRODUCIBILITY
We are committed to ensuring the reproducibility of all experimental results. The code has been made publicly available via GitHub repository:
htgps//gihhbb.oo/wwyweeee////
USE OF LLMS
The authors affirm that the core research, methodology, and scientific findings presented this work were independently conceived and developed by the authors. All figures and tables were created and formatted by the authors themselves. The scientific content, including research ideas, experimental design, and result analysis, not produced by LLMs. LLMs were employed solely assist in improving the clarity and readability of the manuscript.
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APPENDIX
A EXPERIMENT SETUP DETAIL
A.1 DATASET SETTING
For the pre-training phase of ECHO, a comprehensive corpus of 12 public datasets was aggregated, spanning six distinct Brain-Com Computer Interface (BCI) tasks. These include Emotion Recognition, Motor Imagery, Major Depressive Disorder (MDD) detection, workload assessment, event type clas- sification, and attention monitoring. This multi-task, multi-dataset approach designed to expose the model to wide variety of EEG signal characteristics, thereby fostering the development of ro- bust and generalizable representations. All signals were uniformly resampled to 250 Hz to ensure consistency across the corpus. A summary of the detailed information for each dataset is shown in Table 4. The detailed introductions for each task are listed below (includes datasets used by all ECHO variants):
Table 4: Detailed Information of Datasets Used for Pre-training
Task
Dataset
#Subjects
#Chamnels
Duration
Sampling Rate #Classes
SEED-IV
15
62
46
250 Hz
Emotion Recognition
SEED-V
SFRD
15
62
46
250 Hz
BCIIV
22
48
250 Hz
High-
Motor Imagery
Stieger2021-4 -LR
64
15
48
250 Hz
Stiege2201I-II
46
250 Hz
PhysioNet
10s
250 H7
KorcaU
54
62
48
250 Hz
MDD Detection
Workkoad Assersment
Mental Arithmetic
20
56
250 H7
Event Type Classification
TUEV (Bveats)
370
16
58
250 Hz
Attention Monitoring
Atlention
30
48
250 Hz
Sleep Staging
ISRIC SI
100
6
30s
250 Hz
ISRUC
306
Seizure Detection
CHB-MIT
22
23
10s
256 Hz
A.1.1 EMOTION RECOGNITION
This task aims to identify human emotional states from EEG signals. The datasets used involve recordings of subjects exposed to stimuli designed elicit specific emotions.
SEED-V:
The SEED-V dataset focuses on the recognition of five distinct emotional cate- gories: happy, sad, neutral, disgust, and fear. features 62-channel electroencephalo- gram (EEG) recordings acquired from 16 participants. For analytical purposes, these sig- nals are segmented into 1-second windows. should noted that data corruption neces- sitated the exclusion of subject 7, leaving 15 subjects in the SEED-V dataset, which is then partitioned into a 5:5:5 split for training, validation, and testing.
SEED-IV: SEED-IV focuses on the recognition of four emotional states: happiness, sad- ness, fear, and neutrality. Data were collected from 15 subjects who participated in sessions, watching a total of 72 film clips chosen induce these emotions. The data were segmented into 4-second nonoverlapping segments for analysis. Key features extracted include Power Spectral Density (PSD) and Differential Entropy (DE) across five distinct frequency bands: delta, theta, alpha, beta, and gamma
SEED: SEED originally owns 3 labels, Positive, Neutral and Negative. To align with the settings in baseline, we remove Neutral EEG data and transform into a binary classifica- tion task. Data is segmented into 4-second nonoverlapping segments.
A.1.2 MOTOR IMAGERY (MI)
Motor Imagery (MD) is the mental rehearsal of a motor action without any overt physical movement. In the context of EEG-based Brain- Brain-Computer Interfaces (BCIs), this task involves classifying dif-
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ferent imagined movements suchh those of the left hand, right hand, or feet- from the user's brain signals. These imagined actions elicit distinct patterns of neural activity, particularly within the sensorimotor cortex, which can be decoded to control external devices.
BCIIV 2a: This dataset contains recordings from 9 subjects performing cue-based motor imagery task. The task involves four distinct classes: imagined movements of the left hand, right hand, feet, and tongue. EEG data was recorded from 22 channels at sampling rate of 250 Hz, with each MI trial lasting for seconds.
This dataset contains EEG recordings from 14 subjects performing exe- cuted, rather than imagined, motor tasks, with a focus on capturing high-frequency com- ponents of brain activity. The paradigm includes four classes: sequential fanger-tapping of the left hand, finger-tapping of the right hand, repetitive toe clenching representing both feet, and rest condition. Signals were recorded from 128 channels and subsequently downsampled to 250 Hz, with each trial lasting for 4 seconds.
High Gamma:
Stieger2021 (LRR): Part of a study investigating the effects of Windfulness-Basce Stress Reduction (MBSR) BCI skill acquisition, this dataset involves 64 subjects performing a horizontal cursor control task. The paradigm consists two classes: motor imagery of the left hand (to move left) and the right hand (to move right). Data was recorded from 15 channels at 250 Hz and segmented into 4-second trials.
Stieger2021 (UD): Sourced from the same subject pool, this dataset focuses on a vertical cursor control task. includes two classes: motor imagery of both hands (to move up) and voluntary rest (to move down). The recording setup and trial segmentation are identical to the LR dataset.
This dataset provides motor imagery EEG recordings from 109 subjects. The paradigm consists of four classes: imagined movements of the left fist, right fist, both fists, and both feet. Data was recorded from 64 channels with sampling rate of 250 Hz, and trials are segmented into 10-second windows.
PhysioNet:
This dataset features EEG recordings from 54 subjects performing binary-clase motor imagery task. The paradigm involves two classes: imagined movements of the left hand and the right hand. Signals were recorded from 62 channels at a sampling rate of 1000 Hz, with each MI trial lasting for 4 seconds.
KoreaU:
A.1.3 MAJOR DEPRESSIVE DISORDER (MDD) DETECTION
This task focuses on identifying biomarkers for Major Depressive Disorder from EEG signals, typi- cally differentiating between patients with MDD and healthy controls.
Mumtaz: This dataset is designed for MDD detection, containing EEG recordings from 34 patients with MDD and 30 healthy controls during eyes-open and eyes-closed resting states. Signals were recorded from 19 channels following the 10-20 system and were subsequently downsampled to 250 Hz. For analysis, the data is segmented into 5-second windows.
A.1.4 WORKLOAD ASSESSMENT
This task, often framed mental stress detection, aims quantify subject's cognitive load or stress level based on their EEG signals.
Mental Arithmetic:
This dataset supports mental stress detection by recording EEG from 36 subjects under two conditions: a resting state ("no stress")) and an active mental arith- metic task (" ("stress"). The signals were acquired using 20 electrodes and segmented into 5-second windows.
A.1.5 EVENT TYPE CLASSIHICATION
This task involves the classification various event types from clinically annotated EEG recordings, which is crucial for automated analysis and diagnosis.
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TUEV (Events): This clinically annotated corpus is used for multi-class event type clas- sification, including six categories such as spike and sharp wave (SPSW), eye movements (EYEM), and artifacts (ARTF). Signals were recorded using 16 bipolar montage channels and segmented into 5-second windows.
Table 5: Hyperparameters for Model Architecture.
Component
Hyperparameter
Setting
Channels
75
Time points
2500
EEG Sample
Patch dimension
256
Sequence length
10
Window size
100
Step
90
Input dimensions
64, 64, 64, 128] [64, 64, 128, 256]
CNN(Encoder)
Output dimensions
Kernel sizes
{(1, 5), (75, 1), (1, (1, (),
Strides Paddings
1), (1, (I, 1)}
{00, 2), (0, 0), (0, (0,2)
FFN Hidden Size
512
Transformer(Encoder)
Head Number
8
Token Dimension Size
256
Layers
4
Hidden dimension
384
Decoder
Attention Heads
6
Feed-forward dimension
1536
Input dimension
256
Connector
Oulput dimension
384
Activation Function
GELU
A.1.6 ATTENTION MONITORING
This task aims to distinguish between states of attention and inattention using EEG signals.
This dataset was collected from 26 subjects performing Discrimina- tion/Selection Response (DSR) task to assess cognitive attention. Each subject participated in three sessions, with each session consisting of alternating 40-second attention periods and 20-second rest periods. To create balanced binary classification problem (attention vs. inattention), the first 20 seconds of each attention period were used. The data was then segmented into 4-second windows with no overlap.
Attention:
A.1.7 SLEEP STAGING
This task aims to automatically classify a subject's sleep stage by analyzing their EEG signals. The objective is to assign labels such as Wake, REM, and Ron-REM (N1, N2, N3) to sequential epochs of EEG data, which essential for analyzing sleep patterns and quality.
ISRUC S1: This dataset subset of the ISRUC-Sleep collection, designed for sleep stage classification. It contains polysomnographic (PSG) recordings from 100 subjects, including both healthy individuals and patients with sleep disorders. Each recording was visually scored by two human experts, providing labels for different sleep stages. The data includes various electrophysiological signals crucial for sleep analysis.
ISRUC S3: Derived from the same ISRUC- Sleep collection, this dataset shares the same data acquisition and scoring protocols as ISRUC S1. It comprises recordings from 10 subjects, maintaining consistent signal types and label standards.
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A.1.8 SEIZURE DETECTION
This task focuses on identifying seizure events from long-term scalp EEG recordings. The goal is to distinguish ictal segments from interictal background activity, which is essential for epilepsy diagnosis and continuous monitoring in clinical and home settings.
This dataset is part of the PhysioNet EEG collections and contains pediatric scalp EEG recordings from 22 subjects monitored at Boston Children's 's Hospital. Each subject includes multiple continuous sessions sampled at 256 Hz using the international 10-20 system, with approximately 23 to 26 EEG channels. A total of 198 seizure events are annotated by clinical experts, providing detailed temporal boundaries for ictal activity. The dataset is widely used for benchmarking seizure detection and event prediction models.
CHB-MIT:
Table 6: Hyperparameters for Training Process.
Phase
Hyperparrameter
Setting
Epochs
90
Batch size
64
Dropout
0.2
Optimizer
Adam
Warm-up
Learning rate
5e-5
Adam /
(0.9, 0.999)
Adam
1e-8
Scheduler
Custom Cosine Schedule
Minimal learning rate
le-6
Epochs
40
Batch size
48
Dropout
0.1
Optimizer
Adam
Learning rale
5e-5 (Decosler/oomnecttrr) Se-6 (Encuder)
Adam /
(0.9, 0.999)
In-context Training
Adam
le-8
Scheduler
Custom Cosine Decay (via LambdaLR)
Cosine cycle opochs
100
Minimal learning rate factor
0.5
ICL Support Samples (Stage
(fixed)
ICL Support Samples (Stage
Random (0-12)
First Stage epochs
10
EEG Sample Length
30 seconds
A.2 BASELINE SELECTION
We introduce the baseline for comparative experiment in this section. Our baseline includes tradi- tional CNN network, Transformer architecture models as well as recent self-supervised Large EEG Models.
EEGNet (Lawhern et al., 2018): A compact Convolutional Neural Network that introduced depth- wise and separable convolutions create an efficient architecture for EEG classification. It is de- signed to generalize effectively across diverse BCI paradigms, demonstrating robust performance even with limited training data.
BIOT (Yang et al., 2023): A Transformer architecture engineered for robust cross-dataset EEG clas- sification. improves generalization across different subjects and recording settings by employing contrastive learning and domain-invariant attention mechanism to mitigate domain shift effects.
LaBraM (Jiang et al., 2024b): scalable Transformer framework for learning general-purpose EEG representations from extensive datasets. It is pretrained on diverse collection of recordings to capture features that are broadly applicable to downstream BCI tasks, utilizing efficient self-attention and task-specific adapters facilitate fine-tuning.
EEGPT (Wang et al., 2024a): Utilizes a dual self-supervised pretraining approach that combines masked autoencoding with spatio-temporal representation alignment. Its hierarchical design decou- ples spatial and temporal feature extraction for greater computational efficiency and adaptability across different BCI applications.
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CBraMod (Wang et al., 2024b): An EEG foundation model designed to handle the complex de- pendencies in brain signals. It features criss-cross Transformer architecture with parallel attention mechanisms that independently model spatial and temporal relationships within the data.
CodeBrain (Ma et al., 2025): An efficient two-stage EEG foundation model. first employs a novel Tokeeiee generate discrete representations by independently processing temporal and frequency components. Subsequently, its EEGSSM architecture, which integrates structured global convolutions with sliding window attention mechanism, is trained via masked prediction efficiently capture the multi-scale dependencies inherent in brain signals.
A.3 MODEL SETTING
To ensure the reproducibility our work, this section provides a complete and detailed specifica- tion of our model''s architecture. We initialize ECHO's decoder block with (Radford et al., 2022). The following Table enumerates the specific hyperparameter settings for every component of the model pipeline, beginning with the initial EEG sample processing, through the feature extraction and encoding stages (Jiang et al., 2025), and concluding with the Connector and Decoder modules.
A.4 TRAINING & ENVIRONMENT SETTING
Our model is trained in two distinct phases, each with a unique of hyperparameters as specified in Table 6. The process begins with an Encoder Warm-up phase to stabilize the feature extractor. This followed by the main Contextual Training Phase phase, which itself includes staged settings for in context learning. The table details the optimizer configurations, learning rate schedules, and other crucial settings for both phases. The entire training pipeline was executed in a PyTorch Lightning' environment on NVIDIA A100 40G GPUs. For better illustration, here is the pseudo code for the whole training and inference process:
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Algorithm Contextual Training Phase of ECHO
Input: query_eeg. support_pairs, query_text (for training only)
function PREPROCESS(raw .eeg)
Unify channels, filter, etc.
2:
return processed.eeg
3: function ENCODER(pnacessed eeg)
D Convert EEG to sequence of tokens
4: return eeg_tokens
Step 1: Encode all EEG samples into a unified context
5: all_eegs +- [s.eeg for in support_pairs] + [query_eegl
6: ceg_tokens [encoder(preproees((ee)) for eeg in all_eegs]
7: ceg context + concat(eeg tokens)
Step 2: Prepare the initial text sequence
8: support.texts [s.text for in support pairs]
9: sequence <- tokenize(coneat (start tokee support support.texts, texts, query_tokeee (toke))
Step 3: Decoder performs autoregressive prediction
logits decoder(input tokens=text sequence, cross attention context=eeg context)
Step 4: Execute task based on the mode if training then
Update model parameters by calculating loss 12: target_tokens + (okeniizecooocccsuupooeeee query_text, end_token))
11:
13:
loss + fosuticciiiniii tokens)
14:
backpropagate(loss)
15: else (inference)
D Obtain the final result via autoregressive generation
16:
result autoregressiveegeennttetggggiiii return result
17:
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B EXPERIMENT RESULT DETAIL
B.1 RESULT OF ALL DATASETS
As shown in Table 7, ECHO performs slightly below the strongest baseline, CBraMod, the SEED- IV dataset. A key factor behind this result lies in the label overlap between SEED-IV and SEED-V V. which share the same four categories. Within the multi-task unified training framework, ECHO must first infer which paradigm a given EEG sample belongs to before performing classification. In scenarios with highly overlapping label spaces, the model prone to misinterpreting SEED-IV samples as belonging to the SEED-V label set, thereby introducing classification emrrr.. Never- theless, it is worth noting that ECHO still achieves performance comparable to, or in some cases better than, other baselines. For instance, ECHO reaches near-best results in the Weighted FI score, demonstrating degree of robustness. This suggests that even under conditions of significant label overlap across tasks, ECHO maintains strong generalization and resilience.
Table 7: Results on the SEED-IV.
Methods
ACC-B
Kappa
FI-Weighted
EEGNet
0.3684 0.0312
0.1945 + 0.0258
0.3251 0.0390
BIOT
0.3165 0.0388
0.1623 # 0.0183
0.3255 0.0371
EEGPT
0.3520 0.0437
0.1322 # 0.0254
0,3154 # 0.0220
LaBraM
0.2647 0,0219
0. # 0.0308
0.3572 & 0.0243
CBraMod
0,4146 + 0.0228
0.2088 = 0.0344
0.3744 # 0.0454
CodeBrain
0,3641 = 0.0328
0.1685 # 0.0300
0.3341 = 0.0249
ECHO
0.3747 0.0121
0.15 + 0.0029
0.3601 + 0.0037
Note: Bold indicates the best performance. Cyan highlight marks ECHO.
As shown in Table ECHO achieves the overall best performance on the SEED-V dataset, outper- forming all baselines across ACC-B, Kappa, and F1- F1-Weighted. Compared to SEED-IV, SEED-V has less overlap label space with other tasks, which reduces ambiguity in paradigm identifica- tion and allows the model to better exploit its unified modeling capacity. In this setting, ECHO avoids the classification errors caused by label interference and demonstrates strong ability to cap- ture task-specific representations under the multi-task framework. These results indicate that when task paradigms are more clearly separated, ECHO can fully realize its potential and consistently surpass ofte--of-the-at baselines.
Table 8: Results on the SEED-V. V.
Methods
ACC-B
Kappa
FI-Weighted
EEGNet
0.2413 0.0021
0.0592 + 0.0054
0.2317 0.0017
BIOT
0.2245 0.0061
0.0432 # 0.0010
0.2153 0.0038
EEGPT
0.2202 0.0044
0.0496 # 0.0036
0.2301 0.0096
LaBraM
0.2372 0.0053
0,0562 # 0.0028
0.2237 & 0.0078
CBraMod
0.2432 + 0,0046
0,0586 # 0.0059
0.2452 & 0.0043
CodeBrain
0.2447 + 0.0044
0.0610 + 0.0032
0.2411 * 0.0037
ВCHO
0.2484 0.0021
0.0640 + 0.0008
0.2456 + 0.0010
Note: Bold indicates the best performance. Cyan highlight marks ECHO.
As shown in Table 9, ECHO achieves performance comparable to the strongest baselines, while obtaining the best result on the F1- F1-Weighted metric. This indicates that, in motor imagery tasks, ECHO demonstrates an advantage in capturing discriminative features under class imbalance. How- ever, in terms of Balanced Accuracy and Cohen's 's Kappa, ECHO falls slightly behind CBraMod. suggesting that distinguishing between complex categories such as left-right hand and upper-lower limb imagery remains challenging under the cross-subject multi-tas setting. Overall, ECHO main- tains competitive performance and shows robustness on metrics emphasizing intra-class consistency, underscoring its adaptability to motor imagery EEG within the Seq2Seq framework.
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Table 9: Results the BCIC-IV-2a.
Methods
ACC-B
Kappa
F1-Weighted
EEGNet
0,4583 0.0281
0.2937 = 0.0612
0,4265 e 0.0498
BIOT
0,4421 + 0.0415
0.2768 # 0.0387
0.4180 + 0.0634
REGPT
0.4676 + 0.0304
0.2889 = 0.0529
0,4312 # 0.0391
LaBraM
0.4538 0.0468
0.3011 = 0.0432
0.4147 +: 0.0587
CBraMod
0.4816 0.0355
0.3088 # 0.0473
0.4571 + 0.0543
CodeBrain
0.4721 0.0341
0.2984 = 0.0471
0.4478 = 0.0480
ВCHO
0.4763 0.0011
0.3015 + 0.0012
0.4632 0.0002
Note: Bold indicates the best performance. Cyan highlight marks ECHO.
On the Stieger202I-UD dataset (Table 10), ECHO delivers performance largely comparable to other strong baselines. Specifically, it achieves Balanced Accuracy of 0.7311, which is close LaBraM and CodeBrain but still falls short of the best rest-performing CBraMod. For ROC AUC and PR AUC, ECHO does not surpass CBraMod; however, maintains stable results, with a PR AUC of 0.8258 that is competitive with the top baseline. These findings indicate that while ECHO preserves cross- subject generalization in upper- and lower-limb motor imagery tasks, its discriminative capacity somewhat constrained under the more challenging multi-task setting.
Table 10: Results on the Stieger2021-UD.
Methods
ACC-B
ROC AUC
PR AUC
EEGNet
0.6952 0.0125
0.8113 + 0.0068
0.7741 0.0097
BIOT
0.7035 0.0098
0.8237 # 0.0042
0.7895 0.0112
EEGPT
0.7189 0.0153
0.8378 # 0.0056
0.8032 0.0075
LaBraM
0.7274 0.0087
0.8421 + 0.0091
0.8126 0.0051
CBraMod
0.7598 0.0079
0.8622 # 0.0037
0.8524 0.0039
CodeBrain
0.7304 0.0201
0.8143 + 0.0078
0.8121 + 0.0061
ECHO
0.7311 + 0.0001
0.8242 + 0.0013
0.8258 + 0.0001
Note: Bold indicates the best performance. Cyan highlight marks ECHO.
On the TUEV dataset (Table 11), ECHO achieves a Balanced Accuracy of 0.5322, notably out- performing its encoder-only counterpart (ECHO 0.4816). While the absolute performance does not match state-of-the-ae models like CodeBrain, this comparison reveals a crucial insight: the lightweight encoder inherently limits the model's capacity to resolve complex clinical events. How- ever, the proposed ECHO paradigm successfully uplifts this baseline, demonstrating that the gen- erative pre-training strategy effectively enhances representation quality even when the underlying architecture is constrained.
Similar observations upply to the PhysioNet dataset (Table 12). ECHO achieves Balanced Ac- curacy of 0.5667, showing clear improvement over the encoder-only baseline (ECHO 0.5253). Although it trails behind specialized methods like CBraMod, the consistent gain over ECHO vali- dates the efficacy of the methodology. This suggests that while the base encoder struggles with the specific frequency-spatial patterns of this task, the ECHO framework extracts significantly richer features than supervised training alone, proving the value of the paradigm despite architectural lim- itations.
The extended experimental results in the appendix reveal that ECHO's performance varies across tasks and datasets. For SEED-IV and SEED-V emotion recognition tasks, the substantial label over- lap within the SEED family makes it difficult for ECHO to disentangle paradigms and sub-class categories a multi-tase task setting. which in turn leads to relatively weaker performance compared to some single-task basclines. Nevertheless, ECHO is still able to achieve results close to passing the strongest baselines on certain metrics (c.g., Fl-Weighted), highlighting its robustness. On the BCIC-IV-2a dataset, ECHO performs comparably to the strongest baseline and achieves
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Table 11: Results the TUEV dataset.
Methods
ACC-B
Kappa
F1-Weighted
EEGNet
0.3876 0.0143
0,3577 + 0.0155
0.6539 e 0.0120
BIOT
0.5281 + 0.0225
0,5273 # 0.0249
0.7492 & 0.0082
EEGPT
0.6232 = 0.0114
0.6351 # 0.0134
0.8187 +: 0.0063
LaBraM
0.6409 0.0065
0.6637 = 0.0093
0,8312 : 0.0052
CBraMod
0.6671 0.0107
0.6772 # 0.0096
0.8342 & 0.0064
CodeBrain
0.6428 0.0062
0.6912 + 0.0101
0.8362 + 0.0048
ВCHO
0.4816 0.0152
0.4921 = 0.0118
0.7406 0.0094
BCHO
0.5322 0.0045
0.4973 # 0.0032
0.7442 0.0058
Note: Bold indicates the best performance. Cyan highlight marks ECHO methods.
ECHO
denotes the encoder-only baseline.
the best score F1 F1-Weighted, demonstrating its ability to maintain cross-subject generalization in classical motor imagery tasks. For Stieger2021-UD, while ECHO does not surpass the hest base- line in terms of Balanced Accuracy and AUC, it delivers stable performance overall, indicating its robustness in more complex upper- and lower-limb motor imagery scenarios. In summary, these extended results suggest that while ECHO may encounter performance bottlenekss in multi-task and cross-dataset contexts with overlapping task labels, it nevertheless exhibits strong robustness and generalization. This further validates the applicability and potential advantages of its decoder- centric, Seq2Seq design for neural representation learning.
B.2 VALIDATION OF SEQ2SEQ PARADIGM
As shown in Table 13, systematically compares the performance of configurations across multiple downstream tasks to validate the performance of the Seq2Seq paradigm:
No Support: ECHO performs inference without any samples (ICL = 0, and no task sample), relying solely sample modeling and the decoder's intrinsic capacity for prediction. Encoder Only: A conventional paradigm using only the encoder with lightweight classification head.
Both configurations share identical data splits and evaluation protocols, differing only in whether recoder-centric Seq2Seq prediction and contextual support employed. Across 12 datasets and evaluation metrics (36 comparisons in total), No Support outperforms Encoder Only in 29 out of 36 cases and achieves overall superiority 10 out of 12 datasets. The only exceptions are SEED-IV and Mumtaz, where Encoder Only consistently leads, and Mental Arithmetic, where Encoder Only stronger in Balanced Accuracy but No Support surpasses it in ROC AUC and PR AUC. This trend suggests that even without access to support samples, decoder-centric sequential modeling provides significant and stable gains, rather than relying solely on ICL-based retrieval for improvement.
Representative Comparisons and Quantitative Differences:
Table 12: Results on the PhysioNet dataset.
Methods
ACC-B
Kappa
FI-Weighted
EEGNet
0.5814 + 0.0125
0.4468 0.0199
0.5796 0.0115
BEGConformer
0.6049 = 0.0104
0.4736 0.0171
0.6062 0.0095
ST-Transformer
0.6035 + 0.0081
0.4712
0.6053 + 0.0075
BIOT
0.6153 + 0.0154
0.4875 0.0272
0.6158 0.0197
LaBraM
0.6173 + 0.0122
0.4912 0.0192
0.6177 0.0141
CBraMod
0.6417 + 0.0091
0,5222 = 0.0169
0.6427 + 0.0100
ECHO*
0.5253 + 0.01
0.3619 0.0145
0.5177 0.0089
ECHO
0.5667 + 0.0121
0.4214 0.0185
0.5604 0.0109
Note: Bold indicates the best performance. Cyan highlight marks ECHO methods.
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Table 13: Comparison results of No Support and Encoder Only methods on downstream tasks.
SEED-IV
SEED-V
SEED
Methods
ACC-B
Kappa
F1-W
ACC-B
Kappa
FI-W
ACC-B
ROC AUC
PR AUC
No Support
0,3398
0.1174
0,3400
0.2353
0,0466
0.2353
0.7407
0.8488
0,8522
Encoder Only
0.3740
0.1609
0.3684
0.2223
0,0284
0.2196
0.6548
0,7493
0,7592
BCI IV Za
High-Gamma
Stieger201--L.R
Methods
ACC-B
Kappa
ACC-B
ROC AUT
ALC
ROC AUC
AUC
Support
0.4627
0.8438
0.8534
Encoder Only
0.3406
0,8039
0.6123
0.9048
Stieger2021-UD
PhysiuNet
Arilhumetic
Methods
ACC-B
ROC AUC
PR AUC
ACC-B
Kappa
F1-W
ACC-B
ROC AUC
PR AUC
No Support
0.6924
0.8112
0.8117
0,5437
0
0,5318
0.5442
0.6896
0,6897
Encoder Only
0.6058
0.7759
0.7858
0.5253
0.3619
0.5177
0.6008
0.6555
0.6416
Mumtaz
TUEV (Events)
Attention
Methods
ACC-B
ROC AUC
PR AUC
ACC-B
Kappa
FLWW
ACC-B
ROC AUC
PR AUC
No Support
0,9056
0.9745
0.9748 0,9952
0.5214 0,4816
0.5085
0,7489
0.8056
0.8895
0,8955
Encoder Only
0,9698
0.9953
0.4921
0.7406
0.6472
0.7329
0.7367
Note: Bold indicates the best performance between the two methods for each metric.
Motor Imagery: On datasets such as BCI IV 2a, Stiege2202I-LR/UD. and SEED, No Support achieves consistent superiority across all metrics. For example, in BCI IV 2a, Balanced Accu- racy rises from 0.3406 (Encoder Only) to 0.4627 (No Support), a substantial improvement in Stieger2021-LRR the gains in ROC AUC (0.9349 vs. 0.8918) and PR AUC (0.9363 vs. 0.9048) are particularly notable, underscoring stronger ranking ability and robustness to class imbalance.
On TUEV (Events), No Support consistently outperforms Encoder Only across all metrics, indicating that sequential decoding is particularly effective for modeling context dependencies in event-based labeling. Notably, even though the lightweight encoder performs poorly on this dataset- near random- the seq2seq paradigm itself remains effective, demonstrat- ing that the decoding framework can compensate for weak encoder representations.
Event Detection:
Clinical Depression: On the Mumtaz dataset, Encoder Only clearly dominates across all metrics (e.g., balanced accuracy 0.9698 vs. 0.9056), suggesting that in highly homogeneous, binary clin- ical datasets with relatively sharp decision boundaries, an mccodrr-cassifffr paradigm tailored to the task can more easily reach performance ceilings.
Emotion Recognition: On the SEED family dataset, No Support demonstrates clear advantages SEED and remains competitive SEED-V, yet it is surpassed by Encoder Only on SEED-IV. This discrepancy is likely due to overlapping label spaces and paradigms within the SEED family, which introduce ambiguity
paradigm determination. Without support samples, the decoder must simultancously infer the task paradigm and predict labels; the heavy overlap between SEED- IV and SEED-V can induce "paradigm boundary confusion," weakening the advantage of No Support.
Workload Assessment: On Mental Arithmetic, No Support performs better on AUC metrics (0.6896/0.6897 vs. 0.6555/0.6416), but lags slightly in Balanced Accuracy (0.5442 vs. 0.6008). This reflects divergence between ranking quality and thresholded accuracy: while offers better probability calibration and ranking, its fixed-threshold accuracy does not dominate. With post-processing or threshold tuning, the gap in Balanced Accuracy may be further reduced.
Support
In most datasets and evaluation metrics, No Support significantly outperforms Encoder Only. The underlying reason is that ECHO's sequence seauence-to-sqqueee to-sequence decoding paradigm enables it to jointly model the hierarchical relationships among signals, tasks, and labels within unified symbolic space. Even without support samples, ECHO can rely on the pattern- matching mechanisms acquired during training to perform cross-task and pross-paradigm reasoning. This not only enhances the model's
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robustness but also allows it to maintain strong generalization performance in scenarios where task boundaries are ambiguous or data distributions differ.
By contrast, Encoder Only relics conventional discriminative classification head, which is more suitable for single-paradigm or structurally simpler tasks but struggles with generalization and flex- ibility in complex, heterogeneous multi-task settings. Thus, ECHO's superior performance demon- strates that it can autonomously infer task paradigms and predict labels without task-specific prompts while effectively integrating knowledge from diverse datasets through unified modeling. This ca- pability is a key piece of evidence for ECHO's success, showing that it overcomes the limitations of traditional LEMs and achieves stronger adaptability and generalization in multi-task and cross- dataset scenarios.
B.3 ZERO-SHOT PERFORMANCE
As shown Table 14, we evaluated the model performance on the SEED-IV dataset. It is noted that ECHO and the No Support variant included this dataset during the training phase. Conversely, ECHO w/o SEED-IV operated under zero-shot setting where SEED-IV was excluded from the training corpus. Experimental results indicate that while ECHO w/o SEED-IV yields slightly lower metrics compared to CBraMod, the performance gap remains marginal. This suggests that the model retains competitive capability despite the absence of domain-specific training data, reflecting posi- tive generalization potential.
Table 14: Results on SEED-IV Dutssen for ECHO)
Methods
ACC-B
Kappa
F1-Weighted
CBraMod
0.4146 = 0.0228
0.2088 + 0.0344
0.3744 0.0454
ECHO
0.3740 = 0.0152
0.1609 = 0.0085
0.3684 = 0.0110
ECHO (No Support)
0.3398 = 0.0134
0.1 + 0.0062
0.3400 = 0.0125
ECHO w/o SEED-IV
0.3491 = 0.0141
0.1283 # 0.0078
0.3357 = 0.01
Note: Bold indicates the performance. Cyan highlight ECHO
B.4 TRAINING LOSS
Figure 4 illustrates the pretraining loss curve for the Contextual Training Phase of our model, ECHO. The loss exhibits a rapid initial convergence during the first few epochs, followed by gradual and steady decline. A minor spike is observed at the transition between the stages of this phase. We attribute this transient increase to the shift from a fixed to variable number of support samples and the introduction of random EEG data. Notably, the magnitude of this spike is minimal, suggesting that the ECHO decoder had already acquired robust sequence prediction capabilities during Stage 1. Therefore, Stage 2 serves refine this ability, prompting the model to focus more on the nuanced sequential relationships among the EEG samples.
Loss Curve
0.10
0.35
0.25
0.30
0.05
0.00
Epech
Figure 4: The loss curve of ECHO Contextual Training Phase
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C RELATED WORK DETAIL
Traditional EEG decoding pipelines were primarily based on domain-specific feature extraction methods, such common spatial patterns (CSP), in combination with shallow classifiers, includ- ing linear discriminant analysis (LDA) and support vector machines (SVMs) (Lotte et al., 2007; Guler & Ubeyli, 2007). Although computationally efficient, these approaches were constrained by their reliance on prior assumptions about neural dynamics and exhibited limited generalizability across heterogeneous settings. The introduction of deep learning marked a critical turning point. CNN-based methods enabled the direct learning of spatiotemporal features from raw EEG signals (Zhou et al., 2024; Ding et al., 2024), while RNNs, including LSTMs architectures, provided tools for modeling sequential neural dependencies. Despite their effectiveness in task-specific scenar- ios, these architectures often exhibited poor transfcrability, primarily due to variations in electrode configurations, sampling rates, and task paradigms across datasets. This limitation motivated the transition toward LEMs, also referred to as EEG Foundation Models (Zhou et al., 2025a), which aim learn universal representations from extensive collections of unlabeled EEG data.
Current research on LEMs emphasizes self-supervised pretraining strategies that can be broadly categorized into classes: reconstruction-basee and contrastive-based methods. Reconstruction- based approaches encourage models to Iearn temporal and spatial dependencies by predicting masked or future signal segments. For instance, EEGPT (Wang al., 2024a), which employs dual-masking strategy to reconstruct both raw signals and their spatiotemporal representations, and LaBraM (Jiang al., 2024b) and CodeBrain (Ma et al., 2025). which tokenize EEG into discrete neural codes and learns by reconstructing masked tokens. Further innovations are seen in models like CSBrain (Zhou al., 2025b), which uses cross-scale tokenization to handle the multi-resolution characteristics of EEG, and CBraMod (Wang et al., 2024b), which introduces criss-cross trans- former to model complex spatial and temporal dependencies. In contrast, contrastive- bbased methods enhance robustness and discriminability by constructing positive and negative pairs. Models such as BIOT (Yang et al., 2023) and NeuroGPT (Cui et al., 2024) have explored contrastive objectives alongside masked modeling to improve representation quality and improve generalization between subjects and recording conditions while mitigating domain shift.
While existing LEMs have demonstrated the capacity to produce powerful encoders, their de- coding paradigm remains a critical bottleneck. Typically, high-capacity encoders are paired with lightweight classifiers, leading to a mismatch restricting the full exploitation of pretrained repre- sentations. This limitation has led to only using LLMs as encoders, where EEG embeddings are aligned with text embeddings and decoded via instruction-based prompting. Models such Neu- roLM (Jiang et al., 2024a) and UniMind (Lu et al., 2025) exemplify this paradigm by unifying EEG and language representations. However, recent work has highlighted that such L.M encoder-centric approaches remain fundamentally limited, as they shift the EEG-to-label mapping into text space without resolving the inductive bias mismatch between static semantic structures in language and the dynamic temporal patterns inherent to EEG signals.
To overcome these limitations, the ECHO framework introduces a decoder-centric Seq2Seq paradigm. Unlike encoder- or LLM-centric methods, ECHO structures both support and target EEG samples as serialized sequences, thereby enabling ICL directly in the EEG modality. This design equips LEMs with the ability to dynamically adapt to tasks without parameter updates, while preserving disk-iiscrmminaive capacity and cross-tast task generalization. By reframing EEG de- coding contextual sequence modeling, ECHO advances the field beyond prior encoder-focused paradigms and provides a principled pathway to unlock the full potential of EEG foundation models.
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D EXTRA EXPERIMENT
D.1 TASK EXPANSION
D.1.1 SLEEP STAGING
To evaluate ECHO in long-sequence scenarios, we designed a long-sequence variant and incorpo- rated the ISRUC-S1 sleep staging dataset, which is called ECHO Unlike conventional tasks, sleep staging requires classification of complete 30-second segments, placing higher demands poral modeling and cross-segment consistency. For the following two reasons, we did not place the main result in the main text, but placed it in the appendix for reference law and result exploration.
tem-
ECHO c includes fewer datasets. In the multi-task training setup, all tasks must be aligned to the 30s window length of ISRUC-S1. For datasets originally segmented into shorter clips (e.g., : 1s for emotion recognition), this required padding to 30s, inflating their size by up to 30x. Combined with the additional support samples required for ICL, this drastically increased sequence length and computational overhead. To keep training feasible, the long-sequence version of ECHO was trained only on a reduced set of six datasets (Mumtaz2016, 6, SEED-V, ISRUC-S1, High- Gamma, BCIC-IV- 2a, and Mental Arithmetic). As result, this version is reported in the appendix rather than as the main ECHO model in the paper.
ECHO applies a 30s sleep staging paradigm. Baseline methods in sleep staging commonly exploit 10-minute temporal context (20x30s consecutive segments), which provides a significant advantage by modeling long-range dependencies. Extending this setup to ECHO, however, would result in nearly 1-hour equivalent sequences per forward pass once the serialized Seq2Seq struc- ture and ICL support tokens are included. With the simplified DeepConvNet encoder and limited computational resources, this was not feasible. Therefore, ECHO was evaluated under stricter set- ting. relying solely on the current 30s segment without additional temporal context, making its task substantially harder than that of the baselines (baselines: 10min continuous context; ECHO: 30s discrete support tokens).
As shown in Table 15, ECHO achieved ACC-B 0.7311, Kappa 0.6878, and F1- Fe-Whihhted 0.7580. Compared to the strongest baselines CBraMod (ACC-B 0.7865, Kappa 0.7442, F1- Feighttt 0.8011) and CodeBrain (Kappa 0.7476. F1-Weighted 0.8020), ECHO lags by only 0.055, 0.056, and 0.043, respectively. Given that ECHO does not benefit from 10min temporal context and si- multancously handles the extra sequence load from ICL, these gaps are both expected and relatively small. Furthermore, ECHO exhibits extremely low variance (c.g., ACC-B #0,0012, Kappa +0.0032, Fl-Weighted +0.0022), demonstrating strong training stability and robust inference consistency. Overall, despite operating under much stricter conditions, ECHO delivers performance that is still comparable to state-of tfate-of-the-art baselines, validating the feasibility of its decoder-centric Seq2Seq ICL framework for long-sequence sleep staging.
Table 15: Results on the ISRUC-SI,
Methods
ACC-B
Kappa
Fl-Weighted
EEGNet
0.6238 0.0142
0.5921 + 0.0142
0.7032 0.0309
BIOT
0.7527 0.0121
0.7192 # 0.0231
0.7790 0.0146
LaBraM
0.7633 0.0102
0.7231 # 0.0182
0.7810 0.0133
EEGPT
0.0177 0.0177
0.2223 + 0.0227
0.3111 0.0110
CBraMod
0.7865 0.0110
0.7442 # 0.0152
0.8011 0.0099
CodeBrain
0.7835 0.0033
0.7476 # 0.0040
0,8020 0.0018
0.7838 0.0012
0.7303 # 0.0032
0.7893 e 0.0022
Note: Bold indicates the best performance. Cyan highlight marks ECHO.
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D.1.2 MOTOR IMAGERY
In this study, we further designed a task-specialized version of ECHO, denoted as ECHO which was trained exclusively on motor imagery (MI) datasets. The primary motivation was investigate whether datasets of the same task type are complementary and to assess the contribution of individual datasets within this domain. To this end, we trained a full ECHO-MZ model with all MI datasets and reduced version, ECHO (no KoreaU), in which the KoreaU(Leee et al., 2019) dataset was excluded. By evaluating performance on KoreaU, which was unseen during training in the reduced model, we can examine whether removing one dataset significantly undermines generalization. The results show that although excluding KoreaU leads to a slight drop in performance (e.g., ACC-B decreases from around 64% to 62%). ECHO-MZ (no KoreaU) still generalizes effectively to this unseen dataset. This demonstrates that the model captures transferable representations across MI datasets rather than overfitting to any single dataset, thereby highlighting the robustness and cross- dataset generalization capacity of ECHO in saec-speci scenarios.
Table 16: Results on the KoreaU.
Methods
ACC-B
Kappa
F1 Weighted
ECHO MI (no KoreaU)
0.6235 # 0.0118
0.4012 0.0125
0.6154 = 0.0142
ECHO
0.6412 0,0123
0.4289 0.0107
0.6335 0.0131
Note: Cyan highlight marks Mi-spccialized versions of ECHD.
D.1.3 SEIZURE DETECTION
We extend the original ECHO pre-training corpus by incorporating the CHB-MIT epilepsy dataset, yielding the ECHO variant. This addition enables the model to acquire characteristic high- frequency and transient seizure patterns prior to downstream evaluation, addressing the limited clin- ical coverage of the original pre-training setup.
As shown in Table 17, encoder-centric baselines such as CBraMod and CodeBrain achieve the strongest performance (ACC-B and ROC AUC). reflecting the advantage of their large-capacity en- coders in modeling epileptic dynamics. In contrast, EP without support examples underper- forms these extensively pretrained models. However, once support samples are provided, ECHO exhibits substantial improvements across all metrics (ACC-B +0.053, ROC AUC +0.047, PR AUC +0)111), ultimately achieving the highest PR AUC among all methods. This sharp gain surpasses all encoder-centric SOTA basclines and highlights the effectiveness of the ICL mechanism in lever- aging a few support examples to compensate for gaps in seizure-related timeffrequency modeling. Overall, although EC EP' (No Support) trails existing SOTA models in the multi-task pre-training setting, the addition of ICL successfully compensates for the limitations of the lightweight encoder, enabling ECHOFE EP to deliver competitive performance.
Table 17: Results on the CHB-MIT.
Methods
ACC-B
ROC AUC
PR AUC
BIOT
0.7068 0.0457
0.8761 0.0284
0,3277 0.0460
LaBraM
0,7075 0,0358
0,8679 = 0.0199
0.3287 0.0402
EEGPT
0,5481 0.0151
0.8892 0.0066
0.3073 0.0641
CBraMod
0.7398 0.0284
0.8892 0.0154
0,3689 0.0382
CodeBrain
117273 0.0240
0.8961 0.0174
0.4377 + 0.0288
ECHO BF (No Support)
0,5671 0.0078
0.8290 0.0052
0.3872 0.0049
EP
0.6199 0.0040
0.8762 0.0077
0.4985 0.0032
Note: Bold indicates the best performance, Cyan highlight marks BCHO.
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D.2 COMPONENT VERIFICATION
D.2.1 CHANNEL FUSION STRATEGIES COMPARISON
The primary goal of the this comparison is to validate the channel-fusicn method of ECHO, which employs a simple averaging strategy handle channel alignment stress the contribution of the paradigm itself. We compared this approach with two alternative strategies to assess its effective- ness. The first alternative the Full Channel strategy, which utilizes all available electrodes without reduction. The second is the Channel Deletion strategy, which selects channels (the same with Av- erage method) by priority and deletes directly eliminate phase misalignment caused by averaging.
Table 18 presents the performance comparison on the KoreaU and High-Gamma datasets. The results indicate that the averaging strategy maintains stable performance. On the High-Gamma dataset, the averaging method outperforms the Full Channel baseline. On the KoreaU dataset, the performance gap between the averaging strategy and the Full Channel approach remains small. These findings verify that the averaging operation is sufficient and effective design choice for handling channel heterogeneity.
Table 18: Performance comparison of channel fusion strategies on KoreaU and High-Gamma datasets.
KoreaU (62 Channels)
High-Gamme (128 Channels)
Methods
ACC
ROC AUC
PR AUC
ACC
ROC ROC AUC
PR AUC
Full Channel
0.7217
0.8138
0.8079
0.6780
0.7956
0.8209
Average (Ours)
0.7031
0.7806
0.7790
0.6911
0.8418
0.8049
Channel Deletion
0.6983
0.7808
0.7759
0.6476
0.7710
0,8134
D.2.2 SUPPORT SIZE SCALING ANALYSIS
To systematically evaluate how ECHO's in-context learning behaves under different support sample sizes, we conduct a support-scaling sensitivity analysis across downstream tasks. Because datasets differ widely in performance ranges, first apply min-max normalization to each dataset's curve, enabling fair comparison in a unified coordinate space. This allows us to focus on the trend itself rather than absolute accuracy.
As shown in Figure 5, from the overall trend (black averaged curve), increasing the number of support samples does not lead to strietly monotonic improvement. Instead, performance typically peaks around k = 8, after which further increases produce diminishing or even negative returns. This phenomenon is partly attributable to pretraining effects that the decoder may inherently favor shorter sequences and partly reflects a core property of ICL: the model does not always benefit from more examples Zhang et al. (2025). When k becomes too large, cross-subject variability, trial-level fluctuations, and distributional noise can accumulate, making it harder for the model to construct stable label-space mapping and ultimately causing performance drop-off.
At the same time, we observe second class of datasets where performance improves steadily as more support samples are added, shown in Figure 6. These datasets generally have cleaner dis- tributions, higher class separability, and more stable mappings. In such cases, ECHO can exploit additional support samples more effectively, resulting in nearly monotonic gains. This indicates that the usefulness of support samples is highly dependent the intrinsic characteristics of the task: for well-structured datasets with clear decision boundaries, support examples strongly reinforce la- bel semantics; for noisier or more heterogeneous datasets, too many support samples may instead introduce disruptive variance.
In summary, the optimal number of support samples is not universal but closely tied to dataset complexity. For many EEG tasks, a moderate support size (around k = 8) provides the strongest ICL effect, while several cleaner and more separable datasets benefit from larger support sets.
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SEED-IV
Mental Arithmetic
1.0
1.0
Normalized value
0.8
Normalized value
0.8
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.0
2.5
5.0
7.5
10.0
12.5
0.0
2.5
5.0
7.5
10.0
12.5
k
Attention
SEED-V
1.0
1.0
Normalizer value
0.8
Normalized value
0.8
0.6
0.6
0.4
0.4
0.2
0.0
Average (normalized)
0.0
2.5
5.0
7.5
10.0
12.5
0.0
2.5
5.0
7.5
10.0
12.5
Figure 5: Variation of balanced accuracy with respect to the number of support samples. Each sub- plot displays the curve for individual dataset, and the black line indicates the average performance across all datasets. The balanced accuracy values are normalized using min-max scaling to visualize the trends.
Table 19: Results comparison between Seq2Seq paradigm with encoder-centric baselines.
PhysioNet
Mumtaz2016
Stieger2021 JJR
Method
ACC-B
Kappa
FI-W
ACC-B
ROC
PR AUC
ACC-B
ROC AUC
AUC
0.6264
0.9665
ECHO CBraMod
0.5234
E
0.5253
0.3619
0.5177
0.9698
0.9953
0.9952
0,6123
0.8918
0.9048
ECHO
0.5667
0.4214
0.5604
0.9056
0.9745
0.9748
0.8834
0.9349
0.936
Attention
High-Gamma
ISURC
Method
ACC-B
ROC AUC
PR AUC
ACC-B
ROC AUCC
PR AUC
ACC-B
Kappa
FL-W
CBriMed
0.6478
0.7417
0,7468
0.7478
0,8292
0.8314
0.6784
0.5730
0.6716
ECHO-CRraMed
0.7222
0.8100
0,8056
0.7499
0.8481
0.8363
0.6490
0.4974
0.5914
ECHO
0.6472
0.7329
0.7367
0.8039
0.8900
0.8889
0.6868
0.5950
0.6823
BCHO
0.9125
0.7283
0.6560
0.7031
Note: Bold indicates the best performance between the two methods for each metric. Cyan highlight marks ECHO.
D.2.3 SEQ2SEQ PARADIGM EXTENSIBILITY VALIDATION
We include an additional set of experiments to further validate the extensibility and robustness of the proposed Seq2Seq paradigm. The goal is to determine whether the performance of ECHO originates from the Seq2Seq and ICL modeling framework itself, rather than from factors such encoder capacity or tsec-fpe pretraining bias. this end, we replaced the lightweight encoder ECHO with stronger pretrained encoder, CBraMod, and named this hybrid variant ECHO-CBraMod.
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Mumtaz
High-Gamme Gamma
1.0
1.0
Normalized value
0.8
Normalized value
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.0
2.5
5.0
7.5
10.0
12.5
0.0
2.5
5.0
7.5
10.0
12.5
k
SEED
Stieger2021-UD
1.0
1.0
Normalizer M value
0.8
Normalized value
0.8
0.6
0.6
0.4
0.4
0.2
0.0
0.0
Average (normalized)
0.0
2.5
5.0
7.5
10.0
12.5
0.0
2.5
5.0
7.5
10.0
12.5
Figure 6: Variation of balanced accuracy with respect to the number of support samples for datasets that benefit monotonically from larger support sets
This configuration allows us to assess whether the Seq2Seq decoding mechanism remains effective when the representational power of the encoder is substantially enhanced. We compare this variant against the original CBraMod baseline to evaluate the contribution of the Seq2Seq paradigm at the framework level.
Results shown in Table 19 indicate the effectiveness of the Seq2Seq paradigm. In the comparison between CBraMod and SCHO-CBraMer the introduction of the generative decoder leads to per- formance gains datasets such as Attention and High-Gamma. Specifically, ECHO-CBraMod achieves higher accuracy and ROC AUC scores on these tasks compared to the encoder-only CBraMod. Furthermore, when observing the native implementation, the full ECHO model consis- tently outperforms its encoder-only counterpart ECHO across the majority of datasets, including PhysioNet, Stieger2021, and ISURC S3. These observations suggest that the proposed generative framework effectively utilizes learned representations and provides positive improvements indepen- dent of the specific encoder architecture.
D.2.4 MULTI-TASK LEANING COMPARISON
To compare the performance of the Seq2Seq paradigm with the standard multi-task paradigm, we equipped the CBraMod with multiple task-specific classification heads, utilizing ture extractor for joint training. Under this configuration, the total parameter count of CBraMod-MMI approximately twice that ECHO with only six datasets. As shown in Table 20, ECHO achieves leading performance the majority of tasks. Specifically, on the ISRUC S3 and Attention datasets, ECHO maintains stable performance, whereas CBraMod-MH exhibits a substantial decline and pre- dicts randomly. These results suggest that the sequence generation approach adopted by ECHO effectively handles the heterogeneity across tasks, achieving superior generalization with fewer pa- rameters.
shared fea-
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Table 20: Results comparison between the Multi-Task baseline and ECHO.
PhysioNet
Mumtaz2016
Stieger2021 LR
Method
ACC-B
Kappa
FI-W
ACC-B
ROC AUC
PR AUC
ACC-B
ROC AUC
PR AUC
CBraMod-MH
0.6340
0.5119
0.6345
0.8542
0.9507
0.9622
0.8374
0.9286
0,9331
BCHO
0.5667
0.4214
0.5604
0.9056
0.9745
0.9748
0.8534
0,9349
0,9363
Attention
High-Gamma
ISURC S3
Method
ACC-B
ROC AUC
PR AUC
ACC-B
ROC AUC
PR AUC
ACC-B
Kappa
F1-W
CBraMod-MIl MIl
0,5000
0.4593
0.4654
0.7519
0,8426
0.8375
0.2039
0.0005
0.2004
ECHO
0.8194
0.8973
0.8952
0.8552
0.9208
0.9128
0.7283
0.6560
0.7031
Note: Rold indicates the best performance between methods cach
D.3
OTHER ANALYSIS
D.3.1 MODEL COMPUTATIONAL COST ANALYSIS
To evaluate model complexity and efficiency, we compare ECHO with baseline methods in terms of parameter size and computational cost, as shown in Table 21. For fairness, the parameter counts of all baseline models include their classification heads. Since classifier dimensions vary across datasets, we report the average classifier size to ensure consistent comparison.
Structurally, ECHO consists of -NN-Transforrer encoder, fully connected projection layer, and standard Transformer decoder, resulting in a total of 41.99 11.8MM parameters. Despite this capac- ity, ECHO exhibits competitive computational efficiency, requiring only 2.91G MACs and 5.81G FLOPs, which is lower than CodeBrain at 4.37G MACs and EEGPT at 4.89G MACs. Overall, these results show that ECHO reduces inference-time computational overhead while retaining sufficient modeling capacity through its decoder architecture.
Table 21: Model performance comparison on MACs, Parameters, and FLOOS.
Model
MAC's
Params
FLOPs
BENDR
12.51G
959.800 359.84M
25.02G
BIOT
0,255G
3.20M
0.510G
LaBraM
0.67G
6.02M
1.34G
CBraMod
4.21G
4.03M
6.29G
EEGPT
4.89G
25.24M
9.79G
CodeBrain
4.37G
15.17M
8.74G
ECHO
2.91G
41.98M
5.81G
Note: The MACs and FLOPs are calculated based on 1-second input and single-token output.
D.3.2 DECODER SIZE ANALYSIS
To systematically evaluate how model architecture and scale influence ECHO's downstream perfor- mance, we conducted comparative study between two decoder configurations: the standard BCHO with 41M parameters and the larger ECHO-Large with 66M parameters. This experiment aims to address three key questions: (1) Does the Seq2Seq paradigm provide clear advantages
encoder design? (2) Do support samples reliably improve cross-subject generalization? (3) Can enlarging the decoder further enhance model performance? As shown in Table 22, have the following conclusions:
The Seq2Seq paradigm substantially enhances encoder performance. Across all datasets, the encoder trained without the Seq2Seq objective (ECHO²) lags notably behind both ECHO and ECHO-Large. This confirms that Seq2Seq training encourages the model to learn richer and more structured internal mappings, resulting in consistently stronger representations.
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Table 22: Comparison of ICL capabilities of ECHO molecules of different sizes.
SEED-IV
SEED-V
SEED
Methods
ACC-B
Kappa
Fi-W
ACC-B
Kappa
F1-W
ACC-B
ROC AUC
ARC
ECHO
0.3740
0.1609
0.3684
0.2223
0.0284
0.1996
0.6548
0.7493
0.7592
Support)
ECHO
0.3747
0.1595
0.3601
0.2484
0.0640
0.3456
0,9020
0.8962
ECHO- Large (No Support)
0.3447
0.1234
0.3455
0.2422
0.0545
0.2420
0.6778
0,7874
0.7986
BCHO-Large
0,3647
0.1477
0.3599
0.2474
0.0603
0.2474
0.7583
0.8467
0.8479
High-
Stegge22011LLR
Methods
ACC-8
Kappa
F1-W
ACC-8
ROC: AUI
PR AUC
ACC-B
ROC AUC
PR AUC
ECHO
0.3406
0.1242
0.8918
0,9048
ECHO (No Support)
0.1627
0.2836
0,1432
0,8138
0.9125
0,9017
0.8534
0,9349
0.9363
ECHO
0.4763
0.3015
0.4632
0.8552
0.9208
0.9125
0.8534
0.9349
0.936
BCHO-Large (No Support)
0,3950
0.1933
0,3838
0.8368
0.9042
0.8956
0.8494
0.9283
0.9293
ECHO-Large
0:1376
0.2199
0,4353
0.8667
0.9292
0.9220
0.8499
0.9322
0.9336
Stegor2021-UU
PhysioNet
Montal Arithmetic
Kappa
BCHOF
0.6058
0.7759
0,7858
0.5253
0.3619
0.5177
0.6008
0.6555
0.6416
ECHO (No Support)
0.6924
08112
88117
0.5437
0.3918
0.5318
0,5442
0.6896
0.6897
ECHO
0.7311
0,8242
0.828
0.5667
0,4214
0.5604
0,7500
0,7530
BCHo-Large Support)
RCHO-Aagg
0.7185
0.8259
088544
0.5982
0.4039
0.7796
0.7377
Mumtaz
TUEV (Events)
Attention
Methods
ACC-B
ROC AUC
PR AUC
ACC-B
Kappa
FI-W
ACC-B
ROC AUC
AUC
ECHO
0,9698
0.9953
0.9952
0.4816
0.4921
0.7406
0.6872
0.7329
0.7367
Support)
0.9056
ECHO
N/A
NIA
N/A
0.5322
0,5973
0.7442
0.8194
0.8973
0.8952
FCHO- Large Suppoer)
0
0.9844
0.9846
0.4636
0.4391
0.7142
0.7962
0.8795
018883
BCHO- Large
N/A
N/A
N/A
0.5008
0.4906
0.7398
0.8042
0.8864
0.8879
Nore: Bold indicates the best perfomtance for each
metric Unlaset.
ICL training provides stable and meaningful performance gains. Comparing each method with its (No Support) counterpart shows that adding support examples yields consistent improvements across almost all datasets. This demonstrates that ICL enables the model to exploit the relationship between support and query samples, thereby improving prediction quality.
Increasing decoder size does not yield significant performance benefits. ECHO-Large does not outperform the standard ECHO on most tasks, indicating that ECHO's performance ceiling is not constrained by decoder capacity. Instead, the limiting factor lies in the encoder's EEG representa- tions. Since the encoder remains relatively lightweight, with limited ability to capture inter-subject variability, multi-band structure, and long-range dependencies, simply enlarging the decoder cannot compensate for the encoder's ' representational bottleneck.
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E STANDARDIZED CHANNEL SYSTEM
To unify channels settings different datasets, we use a channel map to map all sorts of channels standard channels. Details shown Table 23.
Table 23: Detail channels mapping ECHO. Center refers to the target channel, and Included
Channels refers channels originally from the source datasets.
Center
Included Channels
Center
Included Channels
T9,
Fpl
Fpl.
AFpšh, AFp3h. Fplh. AFpS, AFps
A2
TI0, M2,
Fp2
F22. ATp6h, ATp8h. rp2h. ATp4, AFpó
AF3
AF3, AFFSS, AFSh, AF3h
Fpy
Fpz, AFplh, AFp2h, AFp
AF2, AFp4h,
AF4i, AP6h
FFTIO, FTTI0
AFT
AF7, AFS, APp9h, AFF7h, AF9h, AFTh, AP19, APp7, AFF7
FT7
FT7, FTT9h, FTTZ6, FFT7
AFS
AF6, AFS, AFpiOh, AFF8b, AF8I, AFIOL AFp8, AFp10. AFFS
FTS
FT8. FFTION, FTTSh, FTSh, FTIOh. FFT8, FTT8
AFpz,
CI
Cl. ROCH,, C3h, OCPI
Fy
Fr. FFC1,, Filh
C2
C2, CCP2h, C2h, C4h, CCP2
POOTh. POOSE, OtTh, Olh, POOT, POOS, OII
C3,
CSh, POC3,
12h, Oz. İz, POOLB, POO26, 02h, 1001,P004, POO2, Olz
C4
C4, Côh. CCP4
Oz
CS
C5, FCCSh, 17h, FCCS, CCPS
P1
CPP3h, CPP1, PPO1
C6
C6. FCC66, CCP6
P10
PI0. TPP1O6, PPO1O
CPI
CPI. CCPIb CP2h, CP4h
P2
P2, CPP26, P2h, CPP2, PPO2
F&h. CPPS,
CP3, CCP3G, CPSb, CP3h
P4
P4, CPP4b, PРO4I. P4h, Pét, PPON
CP4
CP4, OCP4
P5
PS. CPPSh, P7h,, CPP5
CP6, CCP6,, CP6h, TP8h, CPP6
P9. TPP9h
CP
CP2. CPIb
POLO
PO10, 12, PPO10L, POO10, CB2
Cz. Clh.
FI
FI, FRCS), APFI, FFC1
PO4
POZ, PO4, POO4), P04h, P0O4
FIO
AFI0, FIO, AFFIO
POS
POS. PPOSE. PO7h, POSh, PFOS
FZ
F2, APF2), FFC4b, F21,
PO6
POG, PPO6,, PO66
F3
E3, FSB. F3h,
F3h. AFFL FFC3
POT
POT, PPO7b. PO0%,. POIL PPO7
F4
14, AlTish, 1'h, AFF4, ITC4
POS
POS, PPORE, POOL0B, POSh, POI06, PPO8
PS, FRCSI, F7h, AFFS, PPCS
PO9
PO9, 11, PPO9., PPO9, POO9,
F6
F6, FFO66, F8h, AFTS, FECS
POz
POL, POz, PPO16, PPO2h, POth, PO2h, PPO CPPs
FFT7h F8, AFFIOb. FFT8&, F10h
F7,
F8
17. T9h. FTT7. T3
AF9, 19, 19h, AIP9
T4
T8. ITT Töh, T10h, TTPS, T4
FC2
FC2, FFC2), FOC4s, FC2h, FC4h, FFC2, FCC2
PS, TPPHb, Psh. PLOh.
FC3
RC3, RC3h
TP10
TP10, TTPI0S, TTPIO, TPP10
FCCI
TTP7k,
FC5
PCS, FT7h, PCSh
TPS
TP8. TIPSh. TP10h, TPPS
FC6
FCC6
TP9
TP9, TIPSI, TIPS, TPP9
PCIS,
34


Reviews:
[{"review_id": "SIfnWtKNwh", "reviewer": "Reviewer_Vc4L", "summary": "The paper proposes ECHO, a decoder-centric sequence-to-sequence (seq2seq) paradigm for EEG modeling that concatenates support samples (with known task and label) and a target sample into one sequence, forcing the model to generate <task> then <y> (then <EOT>). The goal is to overcome the “decoder bottleneck” in Large EEG Models (LEMs) and enable in-context learning (ICL) across heterogeneous datasets. The system includes a channel-template alignment step (same/nearby electrodes averaged; missing padded with zero) and hybrid positional/role encodings. Training uses a curriculum (first fixed 8 support examples, then randomizing the count in [0,12]); testing uses k∈{0,8,12} with no task tokens fed.", "questions": "1.Technical Writing and Presentation\nTypos/formatting should be fixed:\n(1)“proprocess” → preprocess (p.5)\n(2)Missing ) (Page 5)\n(3)Sentence-initial “if” → If (p.5, under Eq.(5))\n(4)“Postional\" → Positional (p.6)\n(5)“encodding\" → \"encoding\" (Page 6, twice)\n(6)Extra ) near Table 12 (p.24)", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Paradigm-level breakthrough: a decoder-centric seq2seq with a fixed solution order\nECHO hard-codes the reasoning path—support → target → generate <task> → generate <y> → <EOT>—and trains via next-token prediction. This lets the model learn within a single sequence space how to reason from examples to the target, instead of relying on a small classifier head or fragile text-bridging.\n\n\n2.Hybrid positional/role encodings that fuse continuous EEG with discrete control tokens\nThe method explicitly distinguishes functional roles (e.g., support vs. target) and balances heterogeneous inputs by combining token-level, sample-level, and text-level encodings. As a result, the decoder preserves fine-grained temporal EEG structure and the logic of <task>/<y> prediction.\n\n\n3.Strong evidence of generalization and “zero-context” robustness\nEven without any support samples, the sequential formulation internalizes the mapping between signal, task, and label. On unseen datasets (e.g., BCIC 2020-T1) it maintains consistent generalization, and under a strict train-once, evaluate-everywhere protocol it can infer both the task and the label without being given <task> at test time.", "weaknesses": "ICL evaluation under-characterized\nTraining uses a two-stage curriculum (8 → random [0,12]), while testing uses k∈{0,8,12}. However, tables report only “No Support” vs an ambiguous “Support” (k=8? k=12? average/best/mix?). There is no k→performance curve (e.g., 0,2,4,8,12 with CIs) and the support sampling protocol (same/cross subject or device, adjacency, fixed indices) is not fully specified. This obscures how performance scales with context size and complicates attribution to the paradigm rather than context amount.\n\n\nAblations do not test core claims\nCurrent ablations toggle only parts of the hybrid positional encodings.Missing:\nSeq2seq necessity & order sensitivity: shuffle support order; swap output order (<y> before <task>); remove support-recap generation; compare with a non-sequential, information-equivalent baseline that ingests the same support/target encodings and predicts via a feed-forward head.\nDecoder capacity: vary layers/hidden/heads/FFN and compare to an equal-capacity hierarchical discriminative baseline (task-head → label-head) to substantiate the “decoder bottleneck” thesis.\nChannel alignment: isolate its contribution via ablation.\n\n\nFairness: multi-task ECHO vs single-task baselines\nECHO trains jointly across tasks and is evaluated everywhere; most baselines are trained/fine-tuned per task. A multi-task baseline (e.g., shared encoder with task-specific heads) or single-task ECHO is needed to separate seq2seq benefits from multi-task training effects.\n\n\nChannel alignment methodology\nAveraging mapped electrodes may smooth meaningful spatial nuances and inject template bias. There is no comparison to stronger alternatives (e.g., spherical/spline interpolation, learnable channel mappings, or permutation-robust architectures), nor pre/post alignment PSD/topography analyses.\n\n\n Important extensions buried in the appendix\nThe long-sequence variant (ECHOL) and the MI-specialized variant (ECHOMI) appear only in the appendix. They should be summarized in the main text (motivation, setup, key findings) to illustrate scope and limitations; ensure variant names match the paper (avoid superscripts like ECHO^L / ECHO^MI).", "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": "lkJirDqNzk", "reviewer": "Reviewer_zEi1", "summary": "ECHO reframes Large EEG Models as a decoder-centric seq2seq problem that serializes support EEG samples, task/label tokens, and a target EEG, letting a Transformer decoder infer the target’s task and label via next-token prediction—thereby enabling in-context and multi-task EEG learning without parameter updates.  Technically, it adds (i) a simple channel-alignment preprocessor to standardize heterogeneous electrode layouts, (ii) a hybrid positional encoding spanning token-, sample-, and text-levels to fuse continuous EEG with discrete control tokens, and (iii) a staged training scheme that progressively increases support examples to instill contextual reasoning.", "questions": "1. Different classification tasks involve varying numbers of classes. How does ECHO accommodate variable class cardinalities within a unified model architecture? I wasn’t able to find an explicit explanation; if it is already covered, could you point me to the relevant section?\n2. Although the overall structure is clear, a few presentation details were confusing. For instance, the output implied by Eq. (11) appears inconsistent with Fig. 2: the figure does not show $S_{\\text{out}}$. I initially interpreted $T_{2S+3}$​ as the classification output, which seems at odds with Eq. (11).\n3. Could the authors clarify why removing textual positional encoding prevents the model from making meaningful predictions? What does a “no valid predictions” look like here? Intuitively, for classification, the worst case should approximate random guessing.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The strengths of this article are as follows:\n\n1. It introduces a decoder-centric paradigm that mitigates the limitations of prior approaches, such as poor generalization and heavy reliance on inductive biases, while enabling in-context learning.\n2. The argumentation is well structured and easy to follow.\n3. The experimental evaluation is comprehensive: it benchmarks ECHO against various baselines and includes ablations on two forms of positional encoding, demonstrating that both are necessary.", "weaknesses": "This paper presents several weaknesses:\n\n1. While the paper rightly highlights the generalization bottlenecks of encoder-centric methods, the proposed method, ECHO appears confined to a single task family—classification. By contrast, encoder-centric approaches can be lightweight and adaptable across prediction, classification, and captioning, which may weakens the paper's contribution.\n2. The proposed approach depends on a pre-trained encoder, atop which a decoder supporting multiple classification tasks is trained. Clarifying how this design substantively departs from an encoder-centric adaptation tailored to general classification would help strengthen the methodological positioning.\n3. The validation of ECHO’s zero-shot capability could be more persuasive. First, using SEED, data visible during training for “zero-shot” prediction may not constitute a strict zero-shot setting. Second, the evaluation is limited to the BCIC 2020-T1 for Motor Imagery task with only one baseline. A broader assessment across additional datasets and stronger, diverse baselines would make the zero-shot claims more compelling.\n4. The paper’s central contribution seems to lie in its decoder-centric training paradigm. To further underscore its robustness, it may be helpful to evaluate additional combinations of encoder and decoder architectures.", "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": "DpPlJVjpPv", "reviewer": "Reviewer_r5rr", "summary": "The paper proposes **ECHO**, a novel **decoder-centric seq2seq paradigm** for Large EEG Models (LEMs), reframing EEG decoding as in-context sequence modeling. It addresses critical limitations in existing LEMs—decoder bottlenecks, poor cross-task generalization, and lack of in-context learning. The method is evaluated on a broad set of EEG datasets with extensive ablation and zero-shot experiments, demonstrating consistent gains over strong baselines.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **Novel paradigm shift**: Introduces the first decoder-centric, seq2seq formulation for EEG modeling, moving beyond encoder-finetune or LLM-as-decoder paradigms.\n\n2. **Effective use of in-context learning**: Demonstrates that EEG models can benefit from discrete support samples for dynamic adaptation without parameter updates.\n\n3. **Comprehensive evaluation**: Multi-dataset, multi-task, zero-shot, and ablation studies provide strong empirical support for generalization and robustness.", "weaknesses": "1. **Unverified channel averaging in Eq. (5)**: The paper assumes averaging over aliased channels (e.g., T9 and M1) is valid, but no validation is provided—phase misalignment may introduce unnatural signal artifacts.\n\n2. **Missing signal normalization**: It is unclear whether the transformed EEG tokens undergo normalization (e.g., z-score), which is crucial for stable training across datasets.\n\n3. **Fixed-length tokenization in Eq. (8)**: Are all EEG signals be split into equal-length K patches? How do you encode patches with different sampling points?\n\n4. **Ambiguity in embedding nature**: It is not clarified whether the EEG token embeddings are continuous or quantized. If continuous, how is cross-entropy loss applied during next-token prediction?\n\n5. **Lack of warm-up stage evaluation**: The warm-up phase resembles standard multi-task supervised learning. Reporting its direct performance (without contextual training) would clarify whether gains stem from multi-task pretraining or true in-context learning.\n\n6. **Biased task selection**: Downstream tasks focus on emotion and motor imagery. Missing key EEG applications like epilepsy detection or sleep staging limits claims of universality.\n\n7. **Incomplete ablation of positional encoding**: The ablation removes sample- and text-level encodings, but does not test a baseline using only a single, global absolute position encoding across the entire sequence—this would better isolate the necessity of the hybrid design.\n\n8. **Limited generalization beyond seen tasks**: The model relies on labels and tasks seen during training. No evidence is shown for few-shot adaptation to entirely new tasks, limiting claims of true generalization.\n\n9. **Unreported model capacity differences**: Baseline models may vary significantly in parameter count. Moreover, no attempt is made to train baselines in the same multi-task setting, risking unfair comparison.\n\n10. **No scaling law analysis for support samples**: The impact of increasing support count (e.g., 0 → 8 → 12) shows diminishing returns, but no systematic study (e.g., performance vs. number of supports) is conducted.", "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": "7cIAip6I0L", "reviewer": "Reviewer_F1QH", "summary": "ECHO proposes a decoder-centric paradigm for Large EEG Models (LEMs) using sequence-to-sequence learning with in-context learning (ICL). Unlike existing encoder-centric or LLM-centric LEMs, ECHO structures EEG samples, tasks, and labels as sequences, allowing the model to adapt to multiple tasks without fine-tuning. Trained on 12 datasets with multi-task learning, ECHO outperforms single-task state-of-the-art baselines and demonstrates zero-shot generalization to unseen datasets.", "questions": "Can you compare ECHO multi-task vs baselines trained in a multi-task manner? (fair comparison)\n\nWhat's the training time and inference time cost?\n\nHow does performance scale with decoder size?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "First to show ICL works for continuous time series (not just discrete text).\nStrong multi-task performance: Beats single-task baselines despite harder setting.\nCode promised, all hyperparameters documented, 5 seeds reported.\nDecoder-centric paradigm enables multi-task learning in single model.", "weaknesses": "Computational cost not fully discussed. Training time not reported for the warm-up 90 epochs + contextual 40 epochs. Need to process support samples every time.\n\nFor ablation studies: Only ablates positional encodings (Figure 3), missing: decoder size, support sample count, sequence length. Why using 8 support samples in Round 1 and 0-12 in Round 2? Lacking comparison with different decoder architectures.\n\nCurrent baselines are mostly single-task models, what about multi-task learning baselines? (shared encoder + task-specific heads) For now it remains unknown if seq2seq is better than standard multi-task learning.", "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": "gp0iLKDtTc", "reviewer": "Reviewer_KNjL", "summary": "This paper proposes ECHO, a decoder centric framework for EEG modeling as a _seq2seq_ model, enabling in-context adaptations to heterogeneous tasks given supporting samples and their labels. This shift paradigm shows promising results across tasks in-distribution or out-of-distribution. However, there is selective reporting of the results in the main body, so I am leaning towards not acceptance.", "questions": "1. Please see weakness number 1. Could the authors elaborate on why the 6 tasks in section 4.2 representative? In general, I believe the it is worth mentioning where the model is not working as good, given there is at least 3 datasets out of 14. Also, I count 14 datasets in total in the pretraining dataset, but it says 12 in the paper (line 329, page 7). Please comment on the difference.\n2. In equation 11, it seems that next-token prediction is conducted for every textual token, given all prior tokens including the support samples ones. Are predicted support label being used in the subsequent generation, or are there any uses of teacher forcing?\n3. Could authors provide more details on the standardized templated channel set used in the pretraining? In the paper, a 75-channel set is advocated, is there a theoretic reason, or it is chosen empirically?\n4. Other than mapping everything to a standardized channel set, have the authors considered other ways to mitigate channel diversity, for example, channel embedding based on channel identity (like unit embedding proposed in POYO+, reference shown below) or coordinates?\n5. Are the reported values in Table 2 using 8 or 12 supporting samples? Either way, it would be interesting to see how the number of supporting samples affect decoding performance.\n6. One substantial difference between the proposed approach and existing LLM decoder is the use of support samples, ultimately transforming the paradigm to meta-learning. But LLM is known for enabling in-context learning, so have authors tried to equip the support samples to LLM decoder as well?\n7. Did the authors use a pretrained model for the warm-up stage? Because in Figure 2 (c), encoder is marked as frozen during warm-up.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- A large EEG model with ICL capability\n- Pretraining across multiple EEG dataset with distinct channel configurations, made possible by a standardization procedure guided by prior knowledge\n- Learned model transferable to unseen datasets", "weaknesses": "1. If I understand correctly, the reported values are not complete. It is mentioned in the appendix that there were 14 datasets involved in pretraining, but only 6 are reported in section 4.1 and 4 more are reported in appendix. Because the proposed model outperformed baselines in the 6 tasks reported in section 4.1 and showed comparable or worse in the 3 of 4 tasks reported in appendix (outperformed by the same baseline in 2 tasks), I believe it is worth mentioning that in the main body of the paper. \n2. Normalization process of diverse EEG channels is guided by prior knowledge, which could limit future scalability when dataset with unseen EEG channel configurations is put in the pretraining data.\n3. Overall, discussion on limitations is lacking.", "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)