An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 12; pp. 3063 - 3073
• Main Authors: Hosni, S. M., Borgheai, S. B., McLinden, J., Shahriari, Y.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amyotrophic lateral sclerosis; Amyotrophic lateral sclerosis (ALS); Blood; brain-computer interface (BCI); Classification; Electroencephalography; Functional magnetic resonance imaging; functional near-infrared spectroscopy (fNIRS); Generalized linear models; Hemodynamic responses; Hemodynamics; Human-computer interface; Indexing; Infrared spectra; Infrared spectroscopy; Interfaces; Medical imaging; Mental task performance; Near infrared radiation; Parameter identification; Performance evaluation; Physiology; Spectroscopy; Statistical models; subject-specific data-driven approach; Support vector machines; Task analysis
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.3038717

Objective: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum in research on motor-imagery (MI)-based brain-computer interfaces (BCIs). However, strikingly, most of the research effort is primarily devoted to enhancing fNIRS-based BCIs for healthy individuals. The ability of patients with amyotrophic lateral sclerosis (ALS), among the main BCI end-users to utilize fNIRS-based hemodynamic responses to efficiently control an MI-based BCI, has not yet been explored. This study aims to quantify subject-specific spatio-temporal characteristics of ALS patients' hemodynamic responses to MI tasks, and to investigate the feasibility of using these responses as a means of communication to control a binary BCI. Methods: Hemodynamic responses were recorded using fNIRS from eight patients with ALS while performing MI-Rest tasks. The generalized linear model (GLM) analysis was conducted to statistically estimate and evaluate individualized spatial activation. Selected channel sets were statistically optimized for classification. Subject-specific discriminative features, including a proposed data-driven estimated coefficient obtained from GLM, and optimized classification parameters were identified and used to further evaluate the performance using a linear support vector machine (SVM) classifier. Results: Inter-subject variations were observed in spatio-temporal characteristics of patients' hemodynamic responses. Using optimized classification parameters and feature sets, all subjects could successfully use their MI hemodynamic responses to control a BCI with an average classification accuracy of 85.4% ± 9.8%. Significance: Our results indicate a promising application of fNIRS-based MI hemodynamic responses to control a binary BCI by ALS patients. These findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.