Filter-Bank Neural Architecture Search (FBNAS) achieved cross-session decoding accuracies of 79.78%, 70.66%, and 68.38% on three EEG datasets, outperforming six state-of-the-art deep learning algorithms.
FBNAS automates network architecture design for individuals in BCI applications, improving decoding performance over state-of-the-art methods.
Individual differences pose a significant challenge in brain-computer interface (BCI) research. Designing a universally applicable network architecture is impractical due to the variability in human brain structure and function. We propose Filter-Bank Neural Architecture Search (FBNAS), an EEG decoding framework that automates network architecture design for individuals. FBNAS uses three temporal cells to process different frequency EEG signals, with dilated convolution kernels in their search spaces. A multi-path NAS algorithm determines optimal architectures for multi-scale feature extraction. We benchmarked FBNAS on three EEG datasets across two BCI paradigms, comparing it to six state-of-the-art deep learning algorithms. FBNAS achieved cross-session decoding accuracies of 79.78%, 70.66%, and 68.38% on the BCIC-IV-2a, OpenBMI, and SEED datasets, respectively, outperforming other methods. Our results show that FBNAS customizes decoding models to address individual differences, enhancing decoding performance and shifting model design from expert-driven to machine-aided. The source code can be found at https://github.com/wang1239435478/FBNAS-master.
Wang et al. (Thu,) conducted a other in EEG decoding for Brain-Computer Interfaces. Filter-Bank Neural Architecture Search (FBNAS) vs. Six state-of-the-art deep learning algorithms was evaluated on Cross-session decoding accuracy. Filter-Bank Neural Architecture Search (FBNAS) achieved cross-session decoding accuracies of 79.78%, 70.66%, and 68.38% on three EEG datasets, outperforming six state-of-the-art deep learning algorithms.