Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition

Guangli Li; Canbiao Wu; Na Tian; Li Zhang; Zhen Liang

cs.LGMar 18, 2026

Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition

Authors: Guangli Li, Canbiao Wu, Na Tian, Li Zhang, Zhen Liang

Organizations: School of Biological Science and Medical Engineering, Hunan University of Technology, Zhuzhou 412008, China. · School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China · Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China · Shenzhen Pengrui Brain Science Technology, Shenzhen 518060, China

Abstract

Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-conditional mismatch and decision boundary distortion, limiting cross-corpus generalization. In this work, we propose a unified Prototype-driven Adversarial Alignment (PAA) framework for cross-corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA-L, which performs prototype-guided local class-conditional alignment; PAA-C, which further incorporates contrastive semantic regularization to enhance intra-class compactness and inter-class separability; and PAA-M, the full boundary-aware configuration that integrates dual relation-aware classifiers within a three-stage adversarial optimization scheme to explicitly refine controversial samples near decision boundaries. By combining prototype-guided subdomain alignment, contrastive discriminative enhancement, and boundary-aware aggregation within a coherent adversarial architecture, the proposed framework reformulates emotion recognition as a relation-driven representation learning problem, reducing sensitivity to label noise and improving cross-domain stability. Extensive experiments on SEED, SEED-IV, and SEED-V demonstrate state-of-the-art performance under four cross-corpus evaluation protocols, with average improvements of 6.72%, 5.59%, 6.69%, and 4.83%, respectively. Furthermore, the proposed framework generalizes effectively to clinical depression identification scenarios, validating its robustness in real-world heterogeneous settings. The source code is available at https://github.com/WuCB-BCI/PAA

Figures & tables

Fig. 1: Evolution of cross-domain feature alignment optimization. (a) Traditional global alignment method. (b) Prototype-guided local class-conditional alignment. (c) Contrastive semantic regularization, where intra-class and inter-class optimization are in opposite directions. (d) Maximize boundary-aware aggregation, dual-classifiers are used to identify and aggregate controversy samples.

Notation	Description
$\mathcal{S}/\mathcal{T}$	Source / Target Domain
$D\left(\cdot\right)$	Domain Discriminator
$f\left(\cdot\right)$	Feature Extractor
$\phi\left(\cdot\right)$	Relational Aware
$\varphi(\cdot)$	Feature Mapping into RKHS
$\mathcal{P}$	Prototype

TABLE I: Frequently used notations and descriptions.

Panel of Figure 3 — Fig. 2: Unified PAA framework. Here, prototype learning is implemented by Eq. 6 ; the Relation-aware Learning is implemented by Eq. 7 . The PAA framework instantiates three progressive configurations: PAA-L performs prototype-guided local class-conditional alignment; PAA-C encourages cross-domain intra-class compactness and cross-domain inter-class separability by contrasting semantic regularization; PAA-M is a fully boundary-aware configuration that integrates the maximize boundary-aware aggregation in the three-stage adversarial optimization scheme.

Fig. 3: The training process of PAA-M. (a) Firstly, PAA-M learns the base representation. (b) Then, ambiguous regions were identified by maximizing discrepancy, and samples from these regions achieved different results on the two classifiers. (c) Finally, the controversial samples are aggregated.

Number	SEED	SEED-IV	SEED-V	Unified	Adopt
1	Positive	Happy	Happy	Positive	✓
2	Neutral	Neutral	Neutral	Neutral	✓
3	Negative	Sad	Sad	Negative	✓
4	—	Fear	Fear	—	✗
5	—	—	Disgust	—	✗

TABLE II: Unified Emotion Label Mapping.

Methods	SEED $\to$ SEED-IV	SEED $\to$ SEED-V	SEED-IV $\to$ SEED	SEED-IV $\to$ SEED-V	SEED-V $\to$ SEED	SEED-V $\to$ SEED-IV	Mean-Acc.
SVM* [ 22 ]	43.14 ± 8.36	39.08 ± 9.08	44.77 ± 8.62	31.44 ± 8.56	37.27 ± 7.00	35.97 ± 7.27	38.75 ± 8.15
K-Means* [ 23 ]	48.57 ± 8.62	45.33 ± 8.62	50.87 ± 6.35	43.37 ± 7.74	42.70 ± 6.65	46.21 ± 7.60	46.18 ± 7.60
KNN* [ 24 ]	41.35 ± 4.63	36.34 ± 4.52	35.09 ± 4.30	26.02 ± 4.44	30.63 ± 4.34	35.63 ± 4.45	34.18 ± 4.45
RF* [ 25 ]	43.26 ± 8.83	44.06 ± 9.19	48.36 ± 8.59	39.99 ± 9.00	44.07 ± 7.94	31.90 ± 8.22	41.94 ± 8.63
KPCA* [ 26 ]	46.20 ± 8.83	47.08 ± 9.31	53.21 ± 8.62	34.40 ± 8.67	37.12 ± 7.63	31.88 ± 7.96	41.65 ± 8.50
CORAL* [ 27 ]	48.58 ± 8.37	44.24 ± 8.62	45.30 ± 7.50	33.90 ± 8.04	36.22 ± 8.44	35.11 ± 8.00	40.56 ± 8.16

TABLE III: The results in Cross-corpus Cross-subjects Single-session Valuation, expressed as (Acc% ± Std%). The dataset combination is represented as (Source Domain

\to

Target Domain). Here, the results of the baseline model reproduced by us are indicated by ’*’.

Methods	SEED $\to$ SEED-IV	SEED $\to$ SEED-V	SEED-IV $\to$ SEED	SEED-IV $\to$ SEED-V	SEED-V $\to$ SEED	SEED-V $\to$ SEED-IV	Mean-Acc.
SVM* [ 22 ]	42.96 ± 8.82	40.07 ± 8.07	41.23 ± 9.18	38.21 ± 9.24	42.82 ± 8.17	38.08 ± 7.39	40.56 ± 8.48
K-Means* [ 23 ]	47.92 ± 8.02	44.21 ± 8.87	51.01 ± 6.91	44.42 ± 8.06	45.77 ± 6.87	46.95 ± 8.00	46.71 ± 7.79
KNN* [ 24 ]	41.94 ± 4.65	43.24 ± 4.41	40.92 ± 4.28	38.99 ± 4.39	34.92 ± 4.33	36.15 ± 4.43	39.36 ± 4.42
RF* [ 25 ]	47.04 ± 8.40	46.09 ± 8.52	55.64 ± 7.13	44.19 ± 8.60	41.94 ± 8.28	37.60 ± 8.46	45.42 ± 8.23
KPCA* [ 26 ]	51.57 ± 8.81	50.73 ± 8.68	56.79 ± 8.01	39.51 ± 8.80	44.51 ± 7.51	37.17 ± 6.56	46.88 ± 8.06
CORAL* [ 27 ]	50.08 ± 8.82	43.53 ± 8.28	42.82 ± 8.31	36.77 ± 7.98	42.83 ± 8.09	38.52 ± 7.60	42.43 ± 8.18

TABLE IV: The results in Cross-corpus Cross-subjects Cross-session Valuation, expressed as (Acc% ± Std%). The dataset combination is represented as (Source Domain

\to

Target Domain). Here, the results of the baseline model reproduced by us are indicated by ’*’.

Methods	SEED $\to$ SEED-IV	SEED $\to$ SEED-V	SEED-IV $\to$ SEED	SEED-IV $\to$ SEED-V	SEED-V $\to$ SEED	SEED-V $\to$ SEED-IV	Mean-Acc.
SA* [ 28 ]	39.29 ± 7.20	31.01 ± 7.13	40.85 ± 6.94	34.46 ± 7.14	31.22 ± 7.69	33.52 ± 7.26	35.06 ± 7.23
TCA* [ 29 ]	42.36 ± 8.00	42.90 ± 8.24	47.48 ± 7.91	34.84 ± 7.82	46.94 ± 5.86	40.82 ± 7.25	42.56 ± 7.51
GFK* [ 30 ]	43.14 ± 8.30	38.00 ± 8.05	49.82 ± 8.38	32.54 ± 8.08	33.03 ± 6.85	34.21 ± 6.48	38.46 ± 7.69
LeNet* [ 31 ]	42.03 ± 8.64	35.22 ± 8.56	51.91 ± 8.20	32.42 ± 7.13	42.86 ± 7.34	35.24 ± 7.83	39.95 ± 7.95
DANN* [ 3 ]	40.19 ± 8.09	40.37 ± 7.69	42.85 ± 7.51	26.73 ± 7.88	37.26 ± 6.58	37.77 ± 8.06	37.53 ± 7.64
DCORAL* [ 32 ]	41.67 ± 7.94	42.30 ± 8.52	46.95 ± 7.78	35.59 ± 7.76	37.73 ± 7.71	36.12 ± 6.93	40.06 ± 7.77

TABLE V: The results in Cross-corpus Cross-subjects Single-session Leave-one-subject-out Cross-valuation, expressed as (Acc% ± Std%). The dataset combination is represented as (Source Domain

\to

Target Domain). Here, the results of the baseline model reproduced by us are indicated by ’*’.

Methods	SEED $\to$ SEED-IV	SEED $\to$ SEED-V	SEED-IV $\to$ SEED	SEED-IV $\to$ SEED-V	SEED-V $\to$ SEED	SEED-V $\to$ SEED-IV	Mean-Acc.
SA* [ 28 ]	45.60 ± 7.52	39.41 ± 7.54	48.21 ± 7.88	35.50 ± 7.52	34.86 ± 7.67	32.76 ± 8.19	39.39 ± 7.72
TCA* [ 29 ]	48.03 ± 8.59	42.23 ± 8.24	52.86 ± 8.32	30.12 ± 8.13	46.64 ± 8.24	35.05 ± 0.76	42.49 ± 7.05
GFK* [ 30 ]	49.06 ± 10.3	45.86 ± 8.23	52.14 ± 7.55	40.48 ± 7.64	42.19 ± 7.96	37.45 ± 7.19	44.53 ± 7.84
LeNet* [ 31 ]	46.01 ± 8.56	41.31 ± 6.66	47.01 ± 8.65	39.10 ± 7.68	45.03 ± 8.19	40.20 ± 7.79	43.11 ± 7.92
DANN* [ 3 ]	44.10 ± 8.69	40.10 ± 8.49	47.32 ± 8.22	41.25 ± 7.75	48.72 ± 8.52	34.35 ± 8.57	42.64 ± 8.37
DCORAL* [ 32 ]	43.76 ± 8.36	44.20 ± 7.62	43.24 ± 7.89	44.63 ± 7.83	44.82 ± 7.40	39.95 ± 7.15	43.43 ± 7.71

TABLE VI: The results in Cross-corpus Cross-subjects Cross-session Leave-one-subject-out Cross-valuation, expressed as (Acc% ± Std%). The dataset combination is represented as (Source Domain

\to

Target Domain). Here, the results of the baseline model reproduced by us are indicated by ’*’.

Fig. 4: Confusion matrices of baseline model PR-PL and proposed PAA-L, PAA-C, and PAA-M. The horizontal axis represents the predicted labels, while the vertical axis represents the true labels.

	NR	SEED $\to$ SEED-IV	SEED $\to$ SEED-V	SEED-IV $\to$ SEED	SEED-IV $\to$ SEED-V	SEED-V $\to$ SEED	SEED-V $\to$ SEED-IV	Mean-Acc.
with	10%	55.51 ± 7.20	53.02 ± 5.22	60.48 ± 9.30	52.01 ± 7.03	67.97 ± 7.05	53.30 ± 8.10	57.04 ± 7.32
RaL	20%	52.16 ± 6.18	52.02 ± 6.15	60.40 ± 7.44	50.56 ± 6.09	66.16 ± 7.44	51.79 ± 6.16	55.52 ± 5.57
	30%	51.95 ± 4.65	51.81 ± 5.29	60.20 ± 8.89	49.34 ± 6.09	65.22 ± 3.03	50.10 ± 5.38	54.77 ± 5.56
	40%	50.03 ± 4.93	51.51 ± 7.72	59.96 ± 9.61	48.62 ± 5.37	64.57 ± 3.11	48.76 ± 5.04	53.91 ± 5.96
	Gap	(-5.48)	(-1.51)	(-0.52)	(-3.39)	(-1.42)	(-4.54)	(-3.13)
with	10%	48.82 ± 7.74	48.90 ± 4.45	54.25 ± 6.57	48.39 ± 4.16	55.92 ± 6.28	45.08 ± 4.13	50.23 ± 5.56

TABLE VII: The performance comparison of Relation-aware Learning (RaL) and traditional Single-Samples Learning (SSL). Here, NR represents the noise ratio. Gap represents the performance gap between NR of 10% and 40%.

Strategy	0%	10%	20%	30%	40%
with RaL	58.90	57.04	55.52	54.77	53.91
with SSL	53.74	50.23	48.11	46.11	44.62
Gap	(-5.16)	(-6.56)	(-7.41)	(-8.66)	(-9.29)

TABLE VIII: Comparative results of noise tolerance in different strtegies. Here, NR represents the noise ratio.

Panel of Figure 13 — Fig. 5: T-SNE visualization of baseline model PR-PL and proposed PAA-L, PAA-C, and PAA-M.

Ablation Setting	PAA-L	PAA-C	PAA-M
w/o RaL in $\mathcal{S}$	55.21 ± 4.84	54.19 ± 6.42	65.11 ± 7.51
w/o RaL in $\mathcal{S}$ and $\mathcal{T}$	50.48 ± 7.46	47.39 ± 4.08	63.66 ± 5.09
w/o Prototype Repr.	54.46 ± 3.66	52.88 ± 6.20	58.62 ± 5.39
w/o Trans. Matrix	58.43 ± 9.11	62.50 ± 7.24	66.72 ± 9.98
w/o Feature Disc.	55.82 ± 5.30	57.44 ± 5.23	59.74 ± 10.6
w/o Stage (2)	—	—	63.25 ± 8.14

TABLE IX: The results of ablation experiment

Panel of Figure 15 — Fig. 6: Results for different parameter settings of the proposed PAA-L and PAA-C.

Panel of Figure 16 — Fig. 7: Performance analysis of the proposed PAA-L, PAA-C, and PAA-M under four experimental protocols.

Methods	SEED $\to$ MDD	SEED-IV $\to$ MDD	SEED-V $\to$ MDD
KNN* [ 24 ]	52.87 ± 4.87	52.75 ± 5.00	58.48 ± 4.09
RF* [ 25 ]	60.41 ± 4.42	48.75 ± 4.98	59.25 ± 5.62
KPCA* [ 26 ]	53.47 ± 5.70	49.30 ± 4.97	57.61 ± 4.07
SA* [ 28 ]	47.40 ± 5.00	48.76 ± 5.99	53.20 ± 4.97
TCA* [ 29 ]	50.78 ± 4.84	49.19 ± 4.28	52.56 ± 4.92
DDC* [ 34 ]	56.46 ± 4.71	55.85 ± 5.67	62.75 ± 3.01

TABLE X: The identification results of the Major Depressive Disorder. Here, the results of the baseline model reproduced by us are indicated by ’*’.

EEG Emotion Recognition

Electroencephalography (EEG) emotion recognition uses brain signals to classify or track affective states across people and recording settings.

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Sep 1, 2025

Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition

Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains limited by inter-subject variability, reliance on target-domain data, and unavoidable label noise. To address these challenges, we propose a Multi-domain Aggregation Transfer learning framework with domain-class prototypes (MAT) for emotion recognition under completely unseen target domains. MAT introduces a feature decoupling module to disentangle class-invariant domain features from domain-invariant class features, enabling more robust and interpretable EEG representations. A Hierarchical-Domain Aggregation (HDA) mechanism based on Maximum Mean Discrepancy (MMD) constructs superdomains to model shared distributional structures across subjects, while adaptive prototype updating refines domain and class prototypes to capture stable intrinsic representations. Moreover, a pairwise learning strategy reformulates classification as similarity estimation between sample pairs, effectively mitigating the effect of label noise. Extensive experiments on three public EEG emotion datasets (SEED, SEED-IV, and SEED-V) show that the accuracy of MAT is improved by 2.87%, 3.84%, and 2.05% compared with the state-of-the-art (SOTA) model for unseen target domains. Our results provide a promising direction for emotion recognition under real-world unseen-subject scenarios.The source code is available at https://github.com/WuCB-BCI/MAT.

Guangli Li, Canbiao Wu, Zhehao Zhou +3

School of Biological Science and Medical Engineering, Hunan University of Technology, Zhuzhou 412008, China · School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China · Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China +1

Cross-Subject EEG Decoding Prototype-Based Classification Domain Generalization Representation Disentanglement EEG Emotion Recognition

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May 29, 2026

UF-AMA: A unified framework for cross-domain emotion recognition via adaptive multimodal alignment

In recent years, emotion recognition based on physiological signals such as electroencephalogram (EEG) has gained considerable attention, as internal physiological data offer greater objectivity and reliability compared to external behavioral data like facial expressions. However, due to distribution shifts caused by individual and contextual differences, along with variations in sample quality across modalities, constructing a cross-domain multimodal emotion recognition model with high generalization and robustness remains a key challenge. In this study, we propose a Unified Framework with Adaptive Multimodal Alignment (UF-AMA) to address cross-subject and cross-session emotion recognition using multimodal physiological signals. First, we construct a cross-modal feature fusion network comprising Transformer encoders and multi-head cross-attention modules, enabling the deep integration of EEG signals and eye-tracking data. Subsequently, we introduce a confidence-aware screening mechanism that dynamically assesses the predictive reliability of each modality branch on target domain samples, partitions samples into different quality subsets, and accordingly applies global consistency alignment and cross-modal distillation. Finally, we propose a multi-level domain adaptation framework that jointly optimizes the marginal and conditional distributions of both local modality-specific and global fusion features, thereby reducing cross-domain distribution shifts at multiple granularities. Extensive experiments on the SEED and SEED-IV datasets demonstrate that UF-AMA achieves state-of-the-art (SOTA) performance in both cross-subject and cross-session tasks. The source code is available at: https://github.com/BetterCoderLab/UF-AMA.

Zheng Wang, Shuo Wang, Junhong Wang

Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230026, China · Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230026, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230026, China

Unsupervised Domain Adaptation Multimodal Emotion Recognition EEG Emotion Recognition Multimodal Fusion Emotion Recognition

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May 21, 2026

Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning

With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based emotion recognition has emerged as an active research area in recent years, owing to its objectivity and high temporal resolution. However, most existing methods focus on optimizing encoder structures to enhance feature extraction capabilities, while paying relatively little attention to similarity calculation strategies, particularly overlooking the potential temporal misalignment of responses among different subjects. To address these shortcomings, this paper draws inspiration from the late interaction mechanism of ColBERT in natural language processing (NLP) and proposes a Temporal Asynchronous Alignment-based Contrastive Learning (TA2CL) framework. This method transforms the traditional global "hard alignment" similarity calculation approach into a fine-grained local matching mechanism, enabling the model to adaptively search for and align "locally highly correlated" segments between two EEG signals, thereby effectively mitigating the effects of inter-subject differences and temporal delays. Experimental results demonstrate that the proposed method achieves strong performance across multiple public datasets. Specifically, on the FACED dataset, it achieves an accuracy of 64.5% for the nine-class classification task and 79.5% for the binary classification task, while on the SEED and SEED-V datasets, it achieves accuracies of 86.4% and 70.1%, respectively, validating the method's effectiveness and generalization capability.

Ying Xie, Yi Zheng, Zehui Xiao +2

School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China · School of Computer Science and Technology, Tianjin University, Tianjin 300354, China

Cross-Subject EEG Decoding Contrastive Learning EEG Emotion Recognition

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Abstract

EEG Emotion Recognition

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