Effects of a dynamic reference frame in mental task classification for EEG-based brain-machine interface - Abundo, Michael Lochinvar S. (2006)
This research investigates the effects of a moving reference frame on the classification of different mental tasks in a Brain-Machine interface (BMI). The BMI uses 8 surface electrodes to measure EEG signals from which a set of features are extracted and fed into a neural network (NN) to classify different mental tasks. We make use of the band powers and power differences of the EEG signals during 5 pre-determined mental tasks as the features for the mental task classifier which is a feed-forward multi-layer perceptron NN with back propagation. The investigation deals with the comparison of the performance of the NN classifier when the BMI user is subjected visually to a non-moving (static) reference and to a moving (dynamic) reference frame while performing the mental tasks. Furthermore, the study presents data on the performance of the neural network in terms of classification accuracy, and training time with various training sets and validation sets.