Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System
Document Type
Article
Date of Original Version
5-1-2020
Abstract
Objective: Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to successfully control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease. Methods: In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). In the second part, we recorded longitudinal data from one patient in the late locked-in state (LIS) who had fully lost eye-gaze control. Using statistical parametric mapping (SPM) and correlation analysis, the optimal channels and hemodynamic features were selected and used in linear discriminant analysis (LDA). Results: Over all the subjects, we obtained an average accuracy of 81.3%±5.7% within comparatively short times (< 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0%±8.9% in the P3S, though not competitive in patients with no substantial visual problems. Our longitudinal analysis showed substantially superior accuracy using the proposed fNIRS-VM protocol (73.2%±2.0%) over the P3S (61.8%±1.5%). Significance: Our findings indicate the potential efficacy of our proposed system for communication and control for late-stage ALS patients.
Publication Title, e.g., Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume
28
Issue
5
Citation/Publisher Attribution
Borgheai, Seyyed B., John Mclinden, Alyssa H. Zisk, Sarah I. Hosni, Roohollah J. Deligani, Mohammadreza Abtahi, Kunal Mankodiya, and Yalda Shahriari. "Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, 5 (2020): 1198-1207. doi: 10.1109/TNSRE.2020.2980772.