Statistical evaluation of factors influencing Inter-session and Inter-subject Variability in EEG-based Brain Computer Interface - Maswanganyi, Rito Clifford; Tu, Chungling; Owolawi, Pius; Du, Shengzhi (IEEE access, 2022)
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 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 (i) concentration level, (ii) selection of independent components(IC), (iii) inter-session variability, (iv) inter-subject variability, and (v) 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 transfer learning (TL) approach is proposed in this study. Furthermore, three classification methods (TL, KNN and NB) are examined and compared to determine whether multi-source neural information can improve the classification accuracy of individual sessions or subjects. Three EEG datasets acquired using different paradigms are used for experiments. The datasets include steady state motion visual evoked potential (SSMVEP), motor imagery (MI) and BCI competition IV-a dataset. Experimental results have shown that selection of independent components has an effect on the IDR. In this case IC-2 and IC-11 achieved a lowest and highest accuracies of 51% and 100% for SSMVEP datasets, while IC-9 and the double-component (IC-2 and IC-13) achieved a lowest and highest accuracies of 40% and 69% for MI datasets respectively. The second experiment demonstrated that higher alpha rhythm, depicted by a lower IDR corresponds to a lower concentration level. While a lower alpha rhythm depicted by a higher IDR corresponds to a higher concentration level. Moreover, variability within sessions can significantly deteriorate intention detection rate across sessions. As such a decline in accuracy from 82% to 61%, and from 56% to 44% was observed across both SSMVEP and MI sessions during inter-session experiment respectively. Integration of samples from different sessions but same subject resulted in a highest accuracy of 65%, 59% and 40% for SSMVEP, MI and BCI competition dataset. Integration of samples from different subjects resulted in a highest accuracy of 65%, 44% and 48% for SSMVEP, BCI competition and MI datasets. When three classifiers are evaluated and compared to determine whether multi-source neural information can improve the classification accuracy of individual sessions and subjects or domains, both KNN and NB achieved highest accuracies of 59% and 52% respectively, while TL showed a significant increase with an accuracy of 98% achieved using SSMVEP sessions. In a similarly manner both KNN and NB achieved highest accuracies of 49% and 42% respectively using SSMVEP subjects, while TL showed a significant increase with an accuracy of 64% achieved. Furthermore, when 9 MI subject acquired from BCI competition dataset, both KNN and NB achieved highest accuracies of 68% and 65% respectively, while a significant increase in accuracy was observed when TL is used with accuracy of 99% achieved. In conclusion, the change of alpha rhythm magnitude among sessions significantly affect the IDR across sessions. While component selection across sessions has significant effects due to non-linear and non-stationary nature of EEG signals. Moreover, merging of ICs from different sessions, and inter-subject factor introduce challenges of overfitting resulting in low IDR. The classification methods are also found critical, because some advanced classification methods can improve the classification accuracy.