An efficient P300-based brain–computer interface for disabled subjects
Open accessAbstract
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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LDA cannot handle various components of ERPs.
“However, such a method cannot handle various components of ERPs [42], [49], [50].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [49]
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A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
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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
- green
- infoscience.epfl.ch
- Licence
- 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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