Development of a robust asynchronous brain-switch using ErrP-based error correction. (11th November 2019)

• Record Type: Journal Article
• Authors: Yousefi, Rozhin; Rezazadeh Sereshkeh, Alborz; Chau, Tom
• Is Part Of: Journal of neural engineering. Volume 16:Number 6(2019:Dec.)
• Publication Date: 2019-11-11
• Subjects: brain–computer interface -- electroencephalography -- error-related potentials -- asynchronous -- cognitive tasks; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab4943
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . The ultimate goal of many brain–computer interface (BCI) research efforts is to provide individuals with severe motor impairments with a communication channel that they can control at will. To achieve this goal, an important system requirement is asynchronous control, whereby users can initiate intentional brain activation in a self-paced rather than system-cued manner. However, to date, asynchronous BCIs have been explored in a minority of BCI studies and their performance is generally below that of system-paced alternatives. In this paper, we present an asynchronous electroencephalography (EEG) BCI that detects a non-motor imagery cognitive task and investigated the possibility of improving its performance using error-related potentials (ErrP). Approach . Ten able-bodied adults attended two sessions of data collection each, one for training and one for testing the BCI. The visual interface consisted of a centrally located cartoon icon. For each participant, an asynchronous BCI differentiated among the idle state and a personally selected cognitive task (mental arithmetic, word generation or figure rotation). The BCI continuously analyzed the EEG data stream and displayed real-time feedback (i.e. icon fell over) upon detection of brain activity indicative of a cognitive task. The BCI also monitored the EEG signals for the presence of error-related potentials following the presentation of feedback. An ErrP classifier was invoked to automatically alter the task classifier outcome when an error-related potential was detected. Main results . The average post-error correction trial success rate across participants, 85% 12%, was significantly higher ( p < 0.05) than that pre-error correction (78% 11%). Significance . Our findings support the addition of ErrP-correction to maximize the performance of asynchronous BCIs..