A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis

Ceci Verbaarschot*, Daniëlle Tump, Andreea Lutu, Marzieh Borhanazad, Jordy Thielen, Philip van den Broek, Jason Farquhar, Janneke Weikamp, Joost Raaphorst, Jan T Groothuis, Peter Desain

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)2404-2415
Number of pages12
JournalClinical Neurophysiology
Issue number10
Early online date27 Jul 2021
Publication statusPublished - Oct 2021


  • ALS
  • BCI
  • Communication
  • EEG
  • Speller
  • cVEP

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