Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks

The reliable classification of Electroencephalogram (EEG) signals is a crucial step towards making EEG-controlled non-invasive Brain-Machine exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from disabled subjects, who lack full motor functionality. The transfer learning training paradigm investigated through this thesis utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.