Cited by 859EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyEmotion and Mood Recognition

An efficient P300-based brain–computer interface for disabled subjects

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Hoffmann, Ulrich, Vesin, Jean-Marc, Ebrahimi, Touradj, Diserens, Karin·2008·Journal of Neuroscience Methods

Abstract

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

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Crossref · 10.1016/j.jneumeth.2007.03.005

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Authors
Hoffmann, U., Vesin, J.-M., Ebrahimi, T., & Diserens, K.
Affiliations
  • École Polytechnique Fédérale de Lausanne (CH)
  • Centre Hospitalier Universitaire Vaudois (CH)
Corresponding author
Hoffmann, Ulrich
Venue
Journal of Neuroscience Methods
Publisher
Elsevier BV
Published
2008-01
Volume
167
Issue
1
Pages
115-125
ISSN
0165-0270 (ISSN-L)
OpenAlex
W2094017272
Cited by
859
References
32
Citations by year
2026: 9 · 2025: 24 · 2024: 29 · 2023: 36 · 2022: 36 · 2021: 58 · 2020: 48 · 2019: 67 · 2018: 55 · 2017: 56 · 2016: 50 · 2015: 56 · 2014: 68 · 2013: 66 · 2012: 68
FWCI
15.03
Type
journal article
Language
en
Open access
Locations
  • Journal of Neuroscience Methods (publishedVersion)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
brain-computer interface, P300 evoked potentials, severely disabled users, classification accuracy, data rate, electrode configuration, machine learning classification
MeSH terms
Adult, Brain, Brain Diseases, Brain Mapping, Electroencephalography, Female, Persons with Disabilities, Disabled Persons, Humans, Male, Middle Aged, Numerical Analysis, Computer-Assisted, Photic Stimulation, Reaction Time, User-Computer Interface, Event-Related Potentials, P300

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