The function predefined convolutional neural network (FPCNN) improved spontaneous EEG decoding accuracy by up to 3.41% and reduced training/testing time significantly.
A novel function predefined convolutional neural network improves the decoding efficiency, interpretability, and computational speed of spontaneous EEG signals for brain-computer interfaces.
A spontaneous electroencephalogram (EEG) -based brain-computer interface (BCI) is an ideal form of brain-computer interaction. The classical decoding methods can achieve classification by using meaningful manual features, but their performance is poor. The neural network (NN) methods have significantly improved the performance, but their interpretability and computational efficiency are much lower than those of the classical methods. This is because NN abandons the strong a priori knowledge of neuroscience and completely relies on training to extract EEG features. How to integrate the characteristics of neural signals into the design of the basic operator of the NNs while retaining its learning ability is the focus of this work. In this work, we proposed a function predefined convolutional NN (FPCNN) to search for the best frequency points and channel weights to decode spontaneous EEG signals. Among the FPCNN, a novel function predefined convolutional (FPC) layer adopts a learnable way to search for the key spatial-frequency parameters of spontaneous EEG, making its parameters have clear physical meanings. Furthermore, a trainable quadrature detector (TQD) based on FPC was constructed, and the quadrature characteristic was utilized to ensure the capture of complex phase change signals. The core contribution of our method lies in the proposal of a novel NN operator for decoding spontaneous EEG, and a quadrature scheme for handling the phase changes of signals. The experimental results show that the proposed FPCNN significantly improves the performance by 2. 09% (^), 3. 08% (^), and 3. 41% (^), respectively, compared with the state-of-the-art (SOTA) methods on three spontaneous EEG datasets. Moreover, the training and testing time cost of FPCNN in a non-GPU environment only takes 67. 96 and 19. 36 s per epoch. Its savings in computing resources and time are very beneficial for EEG processing in diverse environments. In addition, visualization experiments demonstrated the interpretability and stability of the proposed FPCNN. The experimental results show that our method is efficient, stable, and interpretable. This work has effectively improved the decoding efficiency of spontaneous EEG signals and demonstrated the power of combining traditional signal processing methods with NNs.
Fu et al. (2026) studied this question. The function predefined convolutional neural network (FPCNN) improved spontaneous EEG decoding accuracy by up to 3.41% and reduced training/testing time significantly.