A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model

• Published in: IEEE transactions on human-machine systems 2020-08, Vol.50 (4), p.287-297
• Main Authors: Abiri, Reza, Borhani, Soheil, Kilmarx, Justin, Esterwood, Connor, Jiang, Yang, Zhao, Xiaopeng
• Format: Article
• Language: English
• Subjects: Accuracy; Body kinematics; Brain modeling; Brain-computer interface (BCI); Confounding (Statistics); confounding variables; Controllability; cursor control; electroencephalogram (EEG); Electroencephalography; Evaluation; Headphones; Human-computer interface; Imagery; imagined body kinematics; Kinematics; Low cost; Questionnaires; Stability; Training; Usability; User interfaces
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2020.2983848

Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a 2-D EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics with a low-cost wireless EEG headset. In this article, we evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post-BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.