The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Brain-computer interface technologies: from signal to action
Abstract
Here, we present a state-of-the-art review of the research performed on the brain-computer interface (BCI) technologies with a focus on signal processing approaches. BCI can be divided into three main components: signal acquisition, signal processing, and effector device. The signal acquisition component is generally divided into two categories: noninvasive and invasive. For noninvasive, this review focuses on electroencephalogram. For the invasive, the review includes electrocorticography, local field potentials, multiple-unit activity, and single-unit action potentials. Signal processing techniques reviewed are divided into time-frequency methods such as Fourier transform, autoregressive models, wavelets, and Kalman filter and spatiotemporal techniques such as Laplacian filter and common spatial patterns. Additionally, various signal feature classification algorithms are discussed such as linear discriminant analysis, support vector machines, artificial neural networks, and Bayesian classifiers. The article ends with a discussion of challenges facing BCI and concluding remarks on the future of the technology.
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Citations
Cites
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
High-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761 · cited 1×
“LFP reaches to a small group of neurons recording their field potentials (Bansal et al., 2012), whereas MUA and SU record the action potentials of single neurons (Maynard et al., 1997; Carmena et al., 2003; Collinger et al., 2013).”
BackgroundCost of disorders of the brain in Europe 2010
Gustavsson, A., Svensson, M., Jacobi, F., Allgulander, C., Alonso, J., Beghi, E., Dodel, R., Ekman, M., Faravelli, C., Fratiglioni, L., et al. (2011) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2011.08.008 · Cited by 1,723
Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845 · cited 1×
“These methods provide the highest spatial resolution yet are the most invasive, typically causing damage to tissue surrounding the recording site and cells in the area (Navarro et al., 2005).”
MethodBrain-Controlled Interfaces: Movement Restoration with Neural Prosthetics
Schwartz, A. B., Cui, X. T., Weber, D. J., & Moran, D. W. (2006) · Neuron · doi:10.1016/j.neuron.2006.09.019 · Cited by 792
Toward enhanced p300 speller performance
Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.07.017 · Cited by 749 · cited 1×
“The last component is the effector device, which can be a computer cursor (Wu et al., 2003), a prosthetic arm (Hochberg et al., 2006, 2012; Chao et al., 2010; Collinger et al., 2013), a speller (Krusienski et al., 2008), or a wheelchair (Galán et al., 2008; Iturrate et al., 2009; Li et al., 2013).”
BackgroundBCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller
Rakotomamonjy, A., & Guigue, V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915728 · Cited by 512
Cited by
The PREP pipeline: standardized preprocessing for large-scale EEG analysis
Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00016 · Cited by 1,351
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Faust, O., Acharya, U. R., Adeli, H., & Adeli, A. (2015) · Seizure · doi:10.1016/j.seizure.2015.01.012 · Cited by 572
Self-citationSelf-Supervised Learning for Electroencephalography
Rafiei, M. H., Gauthier, L. V., Adeli, H., & Takabi, D. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3190448 · Cited by 317
Self-citationFeature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
Hong, K.-S., Khan, M. J., & Hong, M. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00246 · Cited by 283
Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface
Khan, M. J., Hong, M. J., & Hong, K.-S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00244 · Cited by 280
Metadata
- Authors
- Ortiz-Rosario, A., & Adeli, H.
- Venue
- Reviews in the Neurosciences
- Publisher
- Walter de Gruyter GmbH
- Published
- 2013-01-01
- Volume
- 24
- Issue
- 5
- Pages
- 537-52
- ISSN
- 0334-1763 (ISSN-L)
- 2191-0200
- OpenAlex
- W2092649222
- Cited by
- 213
- References
- 19
- FWCI
- 9.93
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
