A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Verbaarschot, Ceci; Tump, Daniëlle; Lutu, Andreea; Borhanazad, Marzieh; Thielen, Jordy; van den Broek, Philip; Farquhar, Jason; Weikamp, Janneke; Raaphorst, Joost; Groothuis, Jan T.; Desain, Peter
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 10(2021)
• Page Start: 2404
• Page End: 2415
• Publication Date: 2021-10
• Subjects: ALS -- BCI -- Communication -- cVEP -- EEG -- Speller; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2021.07.012
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: A code-modulated visual evoked potentials (cVEP) brain-computer interface (BCI) speller is assessed in healthy subjects and patients with amyotrophic lateral sclerosis (ALS). ALS patients reached a system performance similar to healthy subjects, outperforming other visual BCI spellers in terms of speed. The results support a general usability of cVEPs for ALS patients, opening up a range of possible assistive BCIs. Abstract: Objective: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants. Methods: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers. Results: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%. Conclusions: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers. Significance: These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home.