Integrating active brain-computer interfaces (aBCIs) with passive BCIs (pBCIs) under different frustration levels

The mental state of the users can significantly affect the performance of active brain-computer interfaces (aBCIs). In this work, we aim to adopt passive BCIs (pBCIs) to measure a typical mental state, frustration, which is much relevant to aBCIs. A novel paradigm has been developed that combines both aBCIs and pBCIs under different frustration levels of users. The aBCI in this work is based on classic binary motor imagery (MI). In experiments, a new strategy was implemented that uses visual feedback to induce different levels of frustration. The electroencephalography (EEG) data collected were used for both aBCIs and pBCIs. The pBCI was utilized to assess the frustration level during the aBCI tasks, and the aBCI classification models for different levels of frustration were trained. For pBCI, the filter bank common spatial pattern (FBCSP) feature extraction and support vector machine (SVM) classification were utilized to classify three (i.e., low, moderate, high) frustration levels. For aBCI, the same method (FBCSP+SVM) was used to classify left versus right MI. We also aim to improve the performance of aBCIs in such conditions, so we developed two new methods to incorporate the pBCI results to adapt three MI classifiers to the varying states of frustration. Compared to the conventional approach of directly classifying MI tasks without considering frustration, the two proposed methods increased the mean classification accuracy by 7.40% and 8.62%, respectively. (Compared with the commonly used non-emotional discrimination data, the results are improved by 4.56% and 5.87% respectively.) Within the scope of non-invasive EEG and MI-based aBCI, this study provides, to our knowledge, an initial integrated demonstration in which a frustration-level classifier (pBCI) is trained and then used to adapt MI decoding (aBCI). It should not be taken as a claim of originality beyond this context. Starting from “user subjective perception”, this paper rises to the engineering level of “objective frustration recognition and classification model adaptation”, and makes a contribution to the depth of EEG data analysis and methodological integrity.