Genetic algorithm based deep learning model adaptation for improvising the motor imagery classification

• Main Authors: R, Vishnupriya, Robinson, Neethu, M, Ramasubba Reddy
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Brain-computer interface; Electroencephalography
• Online Access: https://hdl.handle.net/10356/179921
• Institution: Nanyang Technological University

Deep learning methods have proved a promising performance for electroencephalography-based brain-computer interfaces (EEG-BCI). It is particularly encouraging that a subject-independent model can be trained using a large amount of other subjects’ data. Transfer learning methods such as adaptation or fine-tuning can be used on the pre-trained model to improve the performance. This study examined the influence of fine-tuning on the subject-independent model for EEG-based motor imagery (MI) classification using a genetic algorithm (GA). The proposed method is evaluated on the binary class MI dataset from the Korea University EEG dataset. Results show that the proposed GA-based fine-tuning approach statistically improved the average classification accuracy of the baseline model from 84.46% to 87.29%. More interestingly, our approach shows significant improvement in cases where the performance of the baseline model is poor after fine-tuning using other approaches. Further, layer-wise relevance propagation (LRP) is used to analyze the adapted models to gain a deeper understanding of the neurophysiological explanations underlying the model’s decision.