Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

Dongyi He; Bin Jiang; Xiangkai Wang; Yun Zhao; Hongjie Yan; Wai Ting Siok; Nizhuan Wang

eess.SPAug 13, 2026

Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

Authors: Dongyi He, Bin Jiang, Xiangkai Wang, Yun Zhao, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

Organizations: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China · School of Smart Health, Chongqing Polytechnic University of Electronic Technology, Chongqing, 401331, China · Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, 222002, China · Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, 999077, Hong Kong SAR, China

Abstract

Cross subject emotion decoding from electroencephalography EEG requires representations that accommodate individual variability while preserving spatial spectral structure for interpretation. This study introduces EmoDiPyraTrans, a differential graph Transformer that integrates adaptive graph recurrence, differential attention, pyramid fusion and distribution regularization over sequential relative power spectral density graphs. Across SEED, FACED, MAHNOB HCI, DEAP and DREAMER, the model achieved the highest participant mean accuracy and positive class F1 among the evaluated methods, with accuracy and F1 both reaching 0.928 on SEED. On DEP EEG, positive versus neutral accuracy reached 0.802 within healthy controls and 0.704 within participants with depression, compared with 0.591 under healthy to depression transfer and 0.581 with mixed population development. Complementary SEED analyses identified distributed spatial weighting and an alpha centred spectral preference, while configurations averaging six channels retained near full performance. These findings link generalization assessment with model derived candidate signatures to support interpretable EEG emotion decoding, with code available at https://github.com/hdy6438/EmoDiPyraTrans.

Figures & tables

Fig. 1: Overview of EmoDiPyraTrans, its development procedure and temporal rPSD-graph construction. A, Raw EEG is converted to relative power spectral density (rPSD) graphs $\mathcal{G}\in\mathbb{R}^{C\times T\times F}$ (channels $\times$ subsegments $\times$ frequency bands), which are encoded by parallel one- and two-layer adaptive graph convolutional recurrent network (AGCRN) branches and a linear projection. The graph representations are processed by Differential Attention-based Graph Enhancement modules (DiAttGE 1 and DiAttGE 2 ) before pyramid fusion, stacked Differential Attention Blocks and a multilayer perceptron (MLP) classifier are applied. B, After participants are assigned to development and outer-test sets, development data are partitioned into source-training and target-validation streams according to their roles in model development. Source labels supply cross-entropy, while target-validation signals supply maximum mean discrepancy (MMD) and confidence-filtered pseudo-label losses; target-validation labels are reserved for validation accuracy and model selection, with protocol-specific partitions detailed in Section II-E2 . C, AGCRN, comprising an AGCRNCell and NAPL-GCN with adaptive adjacency from learnable node embeddings [ 12 ] . D, Multi-Head Differential Self-Attention (MHDSA) for feature refinement [ 13 ] . E, Multi-Head Differential Cross-Attention (MHDCA) for query-driven channel aggregation and its channel-attention output [ 13 ] . F, Temporal graph construction using sliding-window segmentation and Welch rPSD estimation.

Method	SEED		FACED		MAHNOB-HCI		DEAP		DREAMER
Method	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
LSTM [ 20 ]	$0.733$ $\pm 0.158$	$0.670$ $\pm 0.274$	$0.568$ $\pm 0.063$	$0.700$ $\pm 0.064$	$0.585$ $\pm 0.126$	$0.341$ $\pm 0.274$	$0.517$ $\pm 0.099$	$0.570$ $\pm 0.219$	$0.614$ $\pm 0.106$	$0.125$ $\pm 0.193$
BiDANN [ 21 ]	$0.794$ $\pm 0.165$	$0.777$ $\pm 0.159$	$0.634$ $\pm 0.070$	$0.738$ $\pm 0.064$	—	—	—	—	—	—
DGCNN [ 6 ]	$0.724$ $\pm 0.145$	$0.619$ $\pm 0.311$	$0.562$ $\pm 0.045$	$0.697$ $\pm 0.043$	$0.584$ $\pm 0.104$	$0.376$ $\pm 0.267$	$0.501$ $\pm 0.094$	$0.495$ $\pm 0.275$	$0.577$ $\pm 0.116$	$0.169$ $\pm 0.189$
GCB-Net [ 22 ]	$0.684$ $\pm 0.172$	$0.517$ $\pm 0.357$	$0.565$ $\pm 0.052$	$0.685$ $\pm 0.053$	—	—	—	—	—	—
RGNN [ 23 ]	$0.790$ $\pm 0.148$	$0.802$ $\pm 0.133$	$0.587$ $\pm 0.050$	$0.722^{\dagger}$	—	—	—	—	—	—

TABLE I: Cross-subject emotion classification across five public EEG datasets.

Method	HC-LOSO		DEP-LOSO		HC $\rightarrow$ DEP		HC+DEP $\rightarrow$ DEP LOSO
Method	Acc	F1	Acc	F1	Acc	F1	Acc	F1
LSTM [ 20 ]	$0.650\pm 0.124$	$0.562\pm 0.264$	$0.615\pm 0.114$	$0.576\pm 0.243$	$0.557$	$0.430$	$0.537\pm 0.092$	$0.345\pm 0.280$
DGCNN [ 6 ]	$0.687\pm 0.137$	$0.644\pm 0.248$	$0.577\pm 0.121$	$0.513\pm 0.263$	$0.528$	$0.385$	$0.532\pm 0.059$	$0.312\pm 0.260$
TSception [ 5 ]	$0.647\pm 0.125$	$0.589\pm 0.241$	$0.548\pm 0.094$	$0.455\pm 0.211$	$0.516$	$0.389$	$0.548\pm 0.069$	$0.409\pm 0.232$
TCN [ 25 ]	$0.636\pm 0.127$	$0.561\pm 0.254$	$0.652\pm 0.137$	$0.607\pm 0.269$	$0.570$	$0.460$	$\mathbf{0.600\pm 0.100}$	$0.476\pm 0.261$
Conformer [ 26 ]	$0.610\pm 0.100$	$0.481\pm 0.249$	$0.532\pm 0.089$	$0.568\pm 0.119$	$0.516$	$0.148$	$0.574\pm 0.059$	$0.429\pm 0.192$

TABLE II: Cross-subject positive-versus-neutral classification on DEP-EEG.

Dataset / Protocol	Metric	Full	Component Ablation				Training-Scheme Ablation
Dataset / Protocol	Metric	Full	w/o DA a	w/o NE	w/o NA	w/o NE & NA	w/o MMD	w/o PL	w/o MMD & PL
SEED	Acc	$\mathbf{0.928}$ $\mathbf{\pm 0.055}$	$0.842$ $\pm 0.129$	$0.891$ $\pm 0.100$	$0.921$ $\pm 0.058$	$0.907$ $\pm 0.078$	$0.819$ $\pm 0.123$	$0.925$ $\pm 0.051$	$0.844$ $\pm 0.106$
SEED	F1	$\mathbf{0.928}$ $\mathbf{\pm 0.054}$	$0.830$ $\pm 0.206$	$0.894$ $\pm 0.096$	$0.922$ $\pm 0.058$	$0.910$ $\pm 0.072$	$0.827$ $\pm 0.117$	$0.925$ $\pm 0.051$	$0.830$ $\pm 0.139$
DEP-EEG (HC-LOSO)	Acc	$0.802$ $\pm 0.130$	$0.668$ $\pm 0.149$	$0.810$ $\pm 0.131$	$\mathbf{0.811}$ $\mathbf{\pm 0.120}$	$\mathbf{0.811}$ $\mathbf{\pm 0.116}$	$0.769$ $\pm 0.134$	$0.795$ $\pm 0.134$	$0.768$ $\pm 0.130$
DEP-EEG (HC-LOSO)	F1	$0.797$ $\pm 0.171$	$0.583$ $\pm 0.279$	$0.788$ $\pm 0.185$	$0.804$ $\pm 0.129$	$\mathbf{0.809}$ $\mathbf{\pm 0.119}$	$0.766$ $\pm 0.157$	$0.795$ $\pm 0.169$	$0.761$ $\pm 0.163$
DEP-EEG (DEP-LOSO)	Acc	$0.704$ $\pm 0.094$	$0.575$ $\pm 0.127$	$0.674$ $\pm 0.092$	$\mathbf{0.709}$ $\mathbf{\pm 0.104}$	$0.691$ $\pm 0.104$	$0.623$ $\pm 0.080$	$0.705$ $\pm 0.093$	$0.687$ $\pm 0.093$

TABLE III: Model components and training-scheme ablations of EmoDiPyraTrans.

Fig. 2: Spatial readouts and channel pruning on SEED ( $n=15$ participants). Spatial condition contrasts are negative minus positive (class 0 minus class 1), with aggregation and scaling defined in Section II-D . A, Scaled product of branch-mean attention. B, Across-participant standard deviation of the within-participant counterpart. C, Scaled pooled condition contrast. D, Sample-level standard deviation of scaled contrast vectors. E,F, Mean and across-participant standard deviation of normalized, fold-averaged mask scores. G,H, Mean and across-participant standard deviation of scaled mask-weighted contrasts. I, Standardized mean of the scaled scores. J, $-\log_{10}(q)$ from two-sided one-sample $t$ -tests against the scaled zero, with Benjamini–Hochberg correction over 62 channels, where zero represents the midpoint of each participant’s channel-score range. K, Participant-specific accuracy and positive-emotion F1 after training-fold channel selection and LOSO retraining, with points denoting test participants, lines denoting means and the green curve showing retained-channel counts averaged over LOSO folds. L, Raw participant-mean fused attention at the ten channels selected by J (blue, negative; orange, positive), with boxes showing medians and interquartile ranges and whiskers spanning the 5th–95th percentiles. Exploratory two-sided paired $t$ -tests used Benjamini–Hochberg correction across these ten channels, which were selected and tested within the same cohort; * $q<0.05$ , ** $q<0.01$ , *** $q<0.001$ . M, Example topographies across pruning thresholds.

Fig. 3: Frequency dependence and spatial organization of the SEED-derived signature. A, Accuracy of the full seven-band and leave-one-band-out models. B, Accuracy of single- and two-band inputs. C, Participant-level single-band accuracy ( $n=15$ ; bars, means; error bars, s.d.). D, Correlations between spatial attention-contrast maps from single-band models. E, Similarity among representative multiband models. F, Regional mean absolute attention contrasts for representative models. G, Group-mean positive-minus-negative attention-contrast maps across bands and representative combinations.

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May 30, 2026, cs.LGJ/K move · Enter open · S save

Context-aware tokenization for Cross-subject Emotion Decoding from EEG

Jiaxin Qing, Lexin Li

University of California, Berkeley

Electroencephalography Brain-Computer Interface Emotion Recognition Fourier

Open paper AskPDF arXiv

In cross-subject EEG emotion decoding, neighboring windows can inform the representation of a target window, but they contain both background variation and sustained task signals. This creates a challenge for contextual representation learning because subtracting shared activity can also remove useful information. We introduce the Morlet Spectral Transformer (MST), which conditions target-window tokens on a structured spectral summary before attention. MST averages the Morlet log-amplitude spectra of unlabeled same-trial neighbors and uses frequency-specific electrode projections to combine the target spectrum with its reference-relative residual. This retains access to absolute activity while incorporating context into a fixed attended target-token grid. Without external pretraining, MST achieves 66.5%, 40.7%, 36.1%, and 27.9% accuracy on SEED, SEED-IV, SEED-V, and SEED-VII under leave-one-subject-out evaluation, averaged over three random seeds, outperforming the evaluated pretrained and from-scratch baselines. On FACED, MST achieves 17.52% accuracy in nine-class cross-subject evaluation, exceeding the strongest evaluated baseline by 1.15 percentage points. We also performed the ablation studies across all 15 SEED subjects to evaluate the contribution of different components of MST.

Abstract

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