Towards automated quality assessment measure for EEG signals

EEG signals provide the means to understand how the brain works and they can be used within a wide range of applications; especially BCI applications. The main issue that affects the performance of such applications is the quality of the recorded EEG signal. Noise produced during the recording of the EEG signal impacts directly on the quality of the acquired neural signal. BCI applications performance is susceptible to the quality of the EEG signal. Most BCI research focuses on the effectiveness of the selected features and classifiers. However, the quality of the input EEG signals is determined manually. This paper proposes an automated signal quality assessment method for the EEG signals. The proposed method generates an automated quality measure for each EEG frequency window based on the EEG signal bands characteristics as well as their noise levels. Six scores were developed in this research and the quality of the EEG signal is postulated based on these scores. This EEG quality assessment measure will give researchers an early indication of the quality of the signal. This research will help in testing new BCI algorithms so that the testing could be made on only high quality signals. It will also help BCI applications to react to high quality signals and ignore lower quality ones without the need for manual interference. EEG data acquisition experiments were conducted with different levels of noise and the results show the consistency of our algorithms in estimating the accurate signal quality measure.