Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding

Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study prop...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. PP; pp. 1 - 12
Main Authors: Lu, Bin, Chen, Junxiang, Wang, Fuwang, Wen, Guilin, Fu, Rongrong, Hua, Changchun
Format: Journal Article
Language:English
Published: United States IEEE 27.10.2025
Subjects:
Brain modeling
Causality
Decoding
Deep learning
EEG
Electroencephalography
Feature extraction
Invariant Representation
Manifolds
Motors
Reliability
Riemannian Manifold
Semantics
Symmetric matrices
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2025.3625631