A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals
Open accessAbstract
Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?
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Each BCI paradigm relies on manual specification of signal processing, feature extraction and classification methods, requiring significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.
“each paradigm relies on manual specification of signal processing [7], feature extraction [8] and classification methods [9], a process which often requires significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.”
From EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces (2018) · cites this paper as [7]
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- Authors
- Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E.
- Affiliations
- University of British Columbia (CA)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2007-03-27
- Volume
- 4
- Issue
- 2
- Pages
- R32-R57
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2145947508
- Cited by
- 907
- References
- 5
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 26 · 2023: 32 · 2022: 34 · 2021: 50 · 2020: 46 · 2019: 50 · 2018: 65 · 2017: 62 · 2016: 58 · 2015: 58 · 2014: 76 · 2013: 87 · 2012: 56
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- 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, signal processing algorithms, EEG, electrophysiological signals, signal preprocessing
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Communication Devices for People with Disabilities, Communication Aids for Disabled, Electroencephalography, Evoked Potentials, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, User-Computer Interface
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