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

doi:10.1016/j.clinph.2021.07.012

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 journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	2404-2415
Number of pages	12
Journal	Clinical Neurophysiology
Volume	132
Issue number	10
Early online date	27 Jul 2021
DOIs	https://doi.org/10.1016/j.clinph.2021.07.012
Publication status	Published - Oct 2021

Keywords

ALS
BCI
Communication
EEG
Speller
cVEP

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10.1016/j.clinph.2021.07.012Licence: CC BY

Cite this

@article{c229e0e770464250a6f0010584cc9d2e,

title = "A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis",

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.",

keywords = "ALS, BCI, Communication, EEG, Speller, cVEP",

author = "Ceci Verbaarschot and Dani{\"e}lle Tump and Andreea Lutu and Marzieh Borhanazad and Jordy Thielen and {van den Broek}, Philip and Jason Farquhar and Janneke Weikamp and Joost Raaphorst and Groothuis, {Jan T} and Peter Desain",

year = "2021",

month = oct,

doi = "10.1016/j.clinph.2021.07.012",

language = "English",

volume = "132",

pages = "2404--2415",

journal = "Clinical Neurophysiology",

issn = "1388-2457",

publisher = "Elsevier Ireland Ltd",

number = "10",

}

TY - JOUR

T1 - A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis

AU - Verbaarschot, Ceci

AU - Tump, Daniëlle

AU - Lutu, Andreea

AU - Borhanazad, Marzieh

AU - Thielen, Jordy

AU - van den Broek, Philip

AU - Farquhar, Jason

AU - Weikamp, Janneke

AU - Raaphorst, Joost

AU - Groothuis, Jan T

AU - Desain, Peter

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - ALS

KW - BCI

KW - Communication

KW - EEG

KW - Speller

KW - cVEP

U2 - 10.1016/j.clinph.2021.07.012

DO - 10.1016/j.clinph.2021.07.012

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SN - 1388-2457

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JO - Clinical Neurophysiology

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