Statistical Evaluation of Factors Influencing Inter-Session and Inter-Subject Variability in EEG-Based Brain Computer Interface

作者：Rito Clifford Maswanganyi, Chunling Tu, P. Owolawi, Shengzhi Du · 发表于：IEEE Access · 年份：2022 · DOI：10.1109/ACCESS.2022.3205734 · 被引用次数：23 · 研究领域：Computer Science

A cognitive alteration in the form of diverse mental states has a significant impact on the performance of electroencephalography (EEG) based brain computer interface (BCI). Such alterations include a change in concentration levels commonly recognized as being indicated by the alpha rhythm, drowsiness or mental fatigue which occurs during EEG signal acquisition. Change in mental state give rise to a challenge of variability in EEG characteristics across sessions and subjects. Consequently, this variability constitutes to low intention detection rate (IDR) that renders BCI performance unreliable. This study investigates the impact of multiple factors that lead to the poor performance of the EEG-BCI. Five factors 1) concentration level; 2) selection of independent components(IC); 3) inter-session variability; 4) inter-subject variability; and 5) classification methods on the IDR in EEG based BCI. The alpha rhythm, as the indicator of concentration level, is validated, and the relationship between the alpha rhythm and the IDR is studied among sessions. In addition, ICs are examined to determine their effects on the IDR across sessions. The possibility of two sessions to contain similar EEG characteristics is also examined, where both sessions are acquired from the same subject in different days. Moreover, the possibility of two different subjects to containing similar EEG characteristics is examined. Furthermore, to conquer the challenge of variability in EEG dynamics a feature ...