Biasing the overlapping and non-overlapping sub-windows of EEG recording

Abstract

EEG recording involves having subjects sit on a chair for a couple of hours without being allowed to move and being asked to repeatedly perform various mental, computational, motor imaginary or any other tasks for some specific amount of time. This is a time consuming, boring and complicated procedure during which there is no guarantee that the subject will maintain the proper level of concentration on the requested task at all times, this is apart from the possible muscle activity that might be accidentally generated. This might cause complications in terms of generating signals that do not necessarily contain useful information for classification in the whole tasks time duration. This effect is more likely to appear on recordings in which the task period is longer than usual as in the dataset IVa from BCI competition III in which the task time duration is set to 3.5s. This study investigate the impact of various fragments of time on classification performance. The idea is to improve the classification performance by providing higher concentration on segments of the signal that we assume the subject had better concentration on the task. The results indicate the importance of the middle and end sub-epochs while it illustrate lower performance during the earlier sub-windows.