MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE...

Schizophrenia (SZ) is a severe mental disorder. Using electroencephalogram (EEG) signals for objective and accurate recognition of SZ is critical for timely clinical intervention. However, due to the highly non-stationary nature of EEG signals and the complex spatial correlations of neural activities, existing recognition methods still face key challenges, including incomplete feature extraction, limited computational efficiency, and insufficient modeling of long-range temporal dependencies.

To address these issues, this paper proposes a multi-domain EEG feature fusion network (MDEFusion). By synergistically introducing a half-split crossover sparse autoencoder (HCSAE) and a bidirectional attention long short-term memory (BALSTM) network, the model achieves efficient fusion of multi-domain features and the modeling of long-range temporal dependencies. Specifically, MDEFusion constructs an improved SAE with a half-split crossover mechanism to perform nonlinear cross-fusion of features across the time, frequency, and spatial domains. This effectively compresses high-dimensional redundant information while simultaneously enhancing cross-domain feature interaction capabilities. Furthermore, the BALSTM network is utilized to strengthen the contextual correlation between forward and backward sequences. This enables the precise capture of subtle yet critical pathological dynamic features in EEG signals, effectively mitigating the difficulty of stably modeling long-period temporal dependencies.

Experimental results demonstrate that, compared with state-of-the-art methods, MDEFusion achieves an accuracy of 93.1% on the RepOD dataset and 94.6% on the NNCI dataset.

This paper provides an efficient and reliable EEG analysis tool for the auxiliary diagnosis of schizophrenia, demonstrating significant application value for clinical decision support systems.