Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.

Autor: Borgheai SB; Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States; Neurology Department, Emory University, Atlanta, GA, United States., Zisk AH; Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI, United States., McLinden J; Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States., Mcintyre J; Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States., Sadjadi R; Neurology Department, Massachusetts General Hospital, Boston, MA, United States., Shahriari Y; Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States; Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI, United States. Electronic address: yalda_shahriari@uri.edu.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Jan; Vol. 168, pp. 107658. Date of Electronic Publication: 2023 Nov 02.
DOI: 10.1016/j.compbiomed.2023.107658
Abstrakt: Background: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.
Method: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.
Result: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.
Conclusion: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.
Competing Interests: Declaration of competing interest The authors do not have any declaration of interest.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE