HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910011704213504 |
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| author | Wang, Yinghao Xu, Lintao Yu, Shujian Tartaglione, Enzo Nguyen, Van-Tam |
| author_facet | Wang, Yinghao Xu, Lintao Yu, Shujian Tartaglione, Enzo Nguyen, Van-Tam |
| contents | Electroencephalography (EEG) analysis is critical for brain-computer interfaces and neuroscience, but the intrinsic noise and high dimensionality of EEG signals hinder effective feature learning. We propose a self-supervised framework based on the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), which learns orthonormal EEG representations by enforcing feature decorrelation and reducing redundancy. This design enables robust capture of essential brain dynamics for various EEG recognition tasks. We validate HFMCA on two benchmark datasets, SEED and BCIC-2A, where pretraining with HFMCA consistently outperforms competitive self-supervised baselines, achieving notable gains in classification accuracy. Across diverse EEG tasks, our method demonstrates superior cross-subject generalization under leave-one-subject-out validation, advancing state-of-the-art by 2.71\% on SEED emotion recognition and 2.57\% on BCIC-2A motor imagery classification. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_04681 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding Wang, Yinghao Xu, Lintao Yu, Shujian Tartaglione, Enzo Nguyen, Van-Tam Signal Processing Electroencephalography (EEG) analysis is critical for brain-computer interfaces and neuroscience, but the intrinsic noise and high dimensionality of EEG signals hinder effective feature learning. We propose a self-supervised framework based on the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), which learns orthonormal EEG representations by enforcing feature decorrelation and reducing redundancy. This design enables robust capture of essential brain dynamics for various EEG recognition tasks. We validate HFMCA on two benchmark datasets, SEED and BCIC-2A, where pretraining with HFMCA consistently outperforms competitive self-supervised baselines, achieving notable gains in classification accuracy. Across diverse EEG tasks, our method demonstrates superior cross-subject generalization under leave-one-subject-out validation, advancing state-of-the-art by 2.71\% on SEED emotion recognition and 2.57\% on BCIC-2A motor imagery classification. |
| title | HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.04681 |