Classification of motor task execution speed from EEG data

Official URL: http://dx.doi.org/10.1109/SIU.2016.7496225

It is believed that the obtention of instantaneous intention level from electroencephalogram (EEG) signals and its use as a control signal may increase the benefits gained from the robotic rehabilitation process of stroke patients. This paper investigates a method for classifying the speed of arm movements from EEG recordings of healthy subjects under the assumption that the intention level of a patient may be reflected in motor task execution velocity. Experimental data were collected from eight (four male, four female) healthy volunteers while they were performing right arm movements at two different speeds. We designed an experiment in which the subjects were asked to carry a glass cup in two different environments: nail or cotton. The task speeds for both environments were decided individually by the volunteers; however the nail environment had a maximum speed limit. Participants were warned by a crashing glass audio stimulus if they exceeded the speed limit of the nail environment. As a result, a simple daily life activity was performed at two different speeds as an experimental task. Based on experimental data from eight healthy subjects, we successfully classified two different speed levels and resting state from event related synchronization (ERS) and event related desynchronization (ERD) patterns of EEG signals by linear discriminant analysis (LDA) classifier. Results reveal that LDA can discriminate different velocity levels when six frequency bands of three EEG recording channels were used as the feature vector.