Analysis of error-related potentials in P300 and motor imagery based brain computer interfaces

Official URL: http://risc01.sabanciuniv.edu/record=b1640293 (Table of Contents)

Brain Computer Interface (BCI) systems aim to generate alternative communication pathways for people with disabilities by extracting information directly from the brain. Increasing interest in this field of study has enabled patients to use electroencephalography (EEG) in controlling word processing software such as the P300 speller and prostheses using motor imagery through EEG. Despite achieving successful real-time implementations in these applications, Brain Computer interfaces are subject to errors when interpreting the user's intent. One way of reducing this is by using the Error Related Potential (ErrP). These are signals generated by a person when an error occurs in a BCI system. The knowledge that an error has occurred in a BCI could perhaps be used in strengthening the decision making process of the BCI. Our work aims to understand the effect of different types of user involvement has on ErrP waveforms and classi cation performance in P300 and motor imagery based BCI experiments. Particularly, we collect data in three different settings for both P300 and motor imagery based BCIs and provide an analysis of this data using signal processing and machine learning techniques. We also show how results obtained from the motor imagery based experiments can be used as a basis for a BCI system where motor imagery and Error Related Potentials are classiffied simultaneously. Furthermore, preliminary experiments have been done to classify motor imagery and ErrP in this joint motor imagery and ErrP detection system. We have also investigated the e ect of changes in trial frequency on ErrP classiffication performance in motor imagery based BCI systems.