Complex Valued Multi Domain Features and Its Application in Motor Imagery Classification

This project explores the classification of motor imagery tasks using EEG signals by leveraging both phase and amplitude information. Traditional approaches often focus on amplitude features alone, but incorporating phase information provides a more comprehensive representation of brain activity. We compare existing methods (DNN, CNN, XGBoost) with our proposed approach (Stacking classifier, Decision Tree, Voting Classifier) to demonstrate improved classification accuracy. The system has potential applications in assistive technologies for individuals with motor impairments. The extraction of features from electroencephalography (EEG) signals is a vital step in the classification of motor imagery. However, traditional feature extraction methods for EEG signals either focus either on amplitude or frequency, or the rich phase information. Furthermore, to align with the feature vectors in complex space, we developed a CVELM. Significance testing and separability analysis are employed for real-value features and CVF, the experimental results manifest the effectiveness of the CVF. Meanwhile, we evaluate our proposed method on two publicly available datasets from the representative fields of BCI, notably motor imagery decoding. The proposed model yielded better performance compared with existing state-of-the-art methods in terms of overall classification accuracy.

Keywords: Stacking Classifier, Decision Tree Classifier, voting classifier, XGBoost Classifier.