Classification of Motor Imagery Electroencephalogram

Abstract

The thesis focuses on developing a brain-computer interface (BCI) aimed at differentiating between left-hand and right-hand motor imagery using EEG signals. Its primary objective was to create a scalable and user-specific model for accurately interpreting motor imagery tasks. The study involved the development of one SVM and two advanced deep learning models: the Advanced-EEG-MI-CNN and the Advanced-EEG-MI-CNN-LSTM-Transformer. These models were tested on the Physionet and BCIC IV 2a datasets, as well as on self-gathered data. Results indicated that the SVM model achieved an accuracy of 65% for all subjects and 78% for one subject, while the Advanced-EEG-MI-CNN model achieved an accuracy of 65% for all subjects and 78% for a single subject on average. The Advanced-
EEG-MI-CNN-LSTM-Transformer demonstrated promising results in capturing temporal dependencies, with an accuracy of 80% for all subjects and 92% for a single subject on average. Future work includes enhancing user-specific model implementation, applying Independent Component Analysis (ICA) for cleaner data, and improving data pre-processing to minimize noise.