Adaptive Integrating General and Personalized Features for Enhanced Decoding of Motor Imagery EEG Signals via HyperNet-Based Module

Brain-computer interface (BCI) technology enables communication between humans and devices by reflecting users' status and intentions. Electroencephalography (EEG) signals are utilized to capture brain electrical activity with no surgical operation. When conducting motor imagery (MI), one of the endogenous BCI paradigms, the users imagine the movement of muscles used when performing a certain movement without actual physical movement. However, not all subjects show outstanding classification performance in decoding MI-based EEG signals. We propose the novel method that utilizes the weights of the pre-trained model to generate the personalized weights, effectively combining the general MI features with the personalized features. We used the 5-fold cross-validation for evaluating the performances, and conducted the experiments in 3 different pre-trained models (Top-3, Top-5, and Top-7). We compared the performances of our proposed method using the baseline and the full fine-tuning. In comparison to our proposed method with the baseline, our proposed method achieved the improvement of the average accuracies in all pre-trained models, and those values were 0.123, 0.138, and 0.143, respectively. When comparing our proposed method with the full fine-tuning, the average accuracies of our proposed method were the highest in all pre-trained models, and the differences in the average accuracies were 0.012, 0.019, and 0.009, respectively. Hence, we demonstrated the possibility of improving the precision and effectiveness of the EEG-based systems by reflecting the individual differences in EEG signals among the subjects with low classification accuracy.