A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis
Number of pages
SourceClinical Neurophysiology, 132, 10, (2021), pp. 2404-2415
Article / Letter to editor
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SW OZ DCC AI
SW OZ DCC CO
SW OZ DCC BO
SubjectAction, intention, and motor control; Cognitive artificial intelligence; Radboudumc 3: Disorders of movement DCMN: Donders Center for Medical Neuroscience
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.
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