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Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George

Dissertations (1934 -)

Motor imagery (MI) has been one of the most used paradigms for building brain-computer interfaces (BCI), widely used in neurorehabilitation, for restoring functionality to damaged parts of a neurologically deficient person. The existing motor imagery techniques have largely employed feature extraction techniques such as the power spectral density (PSD) and the common spatial patterns (CSP) before classification, using traditional machine learning algorithms such as support vector machines (SVM) and linear discriminant analysis (LDA). These algorithms are quite limited in their ability to generate feature representations for certain types of signals, limiting the potential for improvements in the decoding process. Also, …