Ricerc@Sapienza

Interpretability, accuracy and a solid neurophysiological basis can be considered as the main requirements for the classification model to monitor motor imagery tasks in post-stroke motor recovery paradigms supported by the brain-computer interface technology. This study aimed at comparing the accuracy performance of different classification approaches applied on a dataset of 15 stroke patients. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performance.

Human computer interface (HCI) has become more and more important in the last few years. This is mainly due to the increase in the technology and in the new possibilities in yielding a help to disabled people. Brain Computer Interfaces (BCI) represent a subset of the HCI systems which use measurements of the voluntary brain activity for driving a communication system mainly useful for severely disabled people. Electroencephalography (EEG) has been intensively used for the measurement of electrical signals related to the brain activity.