Journal of integrative neuroscience · 2026
A Multi-Frequency Self-Supervised Fusion Model for EEG-Based Dementia Classification.
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Sheng Jinhua, Lin Jiaqi, Zhang Qiao, Gong Zhaozhe, Zhang Rong, Wu Ming, Cheng Yuhan, Zhu Xinrui, Sun Zhongyuan
Аннотация
BACKGROUND: Brain source localization technology enables precise characterization of the spatial distribution of neural activity, serving as a crucial tool for exploring the pathological mechanisms underlying dementia. However, effectively integrating complementary diagnostic information from source localization features across multiple frequency bands remains a major challenge to enhancing classification performance and model interpretability.
METHODS: An attention-based multi-frequency self-supervised fusion model (AM-SSF) is proposed to address this issue. Independent contrastive self-supervised encoders are trained for the θ (4-8 Hz), α (8-13 Hz), β (13-30 Hz), and γ (30-48 Hz) frequency bands to learn band-specific latent representations. Then, an attention-guided adaptive fusion module is introduced to dynamically allocate band weights through cross-entropy-based supervised optimization, thereby achieving effective cross-band information integration. Finally, a random forest classifier is employed to evaluate the model's performance in distinguishing Alzheimer's disease (AD) from frontotemporal dementia (FTD).
RESULTS: Experimental results show that the proposed framework achieves a classification accuracy of 93.1% under five-fold cross-validation, significantly outperforming baseline methods such as single-band self-supervised learning (SSL) and average pooling fusion. Further analysis of the attention weight distributions revealed that the θ and β bands contributed most to model decision-making, providing interpretability regarding frequency-specific effects.
CONCLUSIONS: In summary, the proposed AM-SSF model enhances AD and FTD classification performance while offering valuable insights into the discriminative roles of frequency band features.
METHODS: An attention-based multi-frequency self-supervised fusion model (AM-SSF) is proposed to address this issue. Independent contrastive self-supervised encoders are trained for the θ (4-8 Hz), α (8-13 Hz), β (13-30 Hz), and γ (30-48 Hz) frequency bands to learn band-specific latent representations. Then, an attention-guided adaptive fusion module is introduced to dynamically allocate band weights through cross-entropy-based supervised optimization, thereby achieving effective cross-band information integration. Finally, a random forest classifier is employed to evaluate the model's performance in distinguishing Alzheimer's disease (AD) from frontotemporal dementia (FTD).
RESULTS: Experimental results show that the proposed framework achieves a classification accuracy of 93.1% under five-fold cross-validation, significantly outperforming baseline methods such as single-band self-supervised learning (SSL) and average pooling fusion. Further analysis of the attention weight distributions revealed that the θ and β bands contributed most to model decision-making, providing interpretability regarding frequency-specific effects.
CONCLUSIONS: In summary, the proposed AM-SSF model enhances AD and FTD classification performance while offering valuable insights into the discriminative roles of frequency band features.