Papers › A novel Fourier Adjacency Transformer for advanced EEG emotion recognition

A novel Fourier Adjacency Transformer for advanced EEG emotion recognition

28 Feb 2025arXiv:2503.13465archive 2025-07-28

Jinfeng Wang, Yanhao Huang, Sifan Song, Boqian Wang, Jionglong Su, Jiaman Ding

EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion classification. In this study, we present the Fourier Adjacency Transformer, a novel framework that seamlessly integrates Fourier-based periodic analysis with graph-driven structural modeling. Our method first leverages novel Fourier-inspired modules to extract periodic features from embedded EEG signals, effectively decoupling them from aperiodic components. Subsequently, we employ an adjacency attention scheme to reinforce universal inter-channel correlation patterns, coupling these patterns with their sample-based counterparts. Empirical evaluations on SEED and DEAP datasets demonstrate that our method surpasses existing state-of-the-art techniques, achieving an improvement of approximately 6.5% in recognition accuracy. By unifying periodicity and structural insights, this framework offers a promising direction for future research in EEG emotion analysis.

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count_parameters YanhaoHuang23/FAT/SEED_VII_dependent.py official repository ran · honoured contract MIT (permissive) · f6b944f50d3f15ae · report
add_periodic_perturbation YanhaoHuang23/FAT/Augmentation.py official repository unverified MIT (permissive) · 5055ad6c1dfe5176 · report
add_remaining_self_loops YanhaoHuang23/FAT/Model/RGNN_model.py official repository unverified MIT (permissive) · a5aac3027975cd7f · report
feature_trans YanhaoHuang23/FAT/Model/PGCN_model.py official repository unverified MIT (permissive) · df022dd5603cf104 · report
feature_trans_2 YanhaoHuang23/FAT/Model/PGCN_model.py official repository unverified MIT (permissive) · a2c4d80e59a71e37 · report
generate_cheby_adj YanhaoHuang23/FAT/utils.py official repository unverified MIT (permissive) · 296c616ab1ad52dc · report
get_model_size YanhaoHuang23/FAT/SEED_VII_dependent.py official repository unverified MIT (permissive) · f01ef84bffad2c9f · report
load_DE_SEED YanhaoHuang23/FAT/utils.py official repository unverified MIT (permissive) · b495ed95d0d79ed0 · report
location_trans YanhaoHuang23/FAT/Model/PGCN_model.py official repository unverified MIT (permissive) · ccdf9efc3bb791ed · report
maybe_num_nodes YanhaoHuang23/FAT/Model/RGNN_model.py official repository unverified MIT (permissive) · ef93260b5137f73b · report
mixup YanhaoHuang23/FAT/Augmentation.py official repository unverified MIT (permissive) · 2b63168cfbfdc309 · report
normalize_A YanhaoHuang23/FAT/utils.py official repository unverified MIT (permissive) · 92a06e528fe1b652 · report
scale_frequency_bands YanhaoHuang23/FAT/Augmentation.py official repository unverified MIT (permissive) · 743d2a041a650c59 · report
scatter_add YanhaoHuang23/FAT/Model/RGNN_model.py official repository unverified MIT (permissive) · bf0ef31097e9c705 · report

Tasks

EEGEEG Emotion RecognitionEmotion ClassificationEmotion Recognition

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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