Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
Abstract
In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel Filter Bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.
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FBCSP is a widely used method to decode oscillatory EEG data, for example, with respect to movement-related information.
“FBCSP [Ang et al., 2008; Chin et al., 2009] is a widely used method to decode oscillatory EEG data, for example, with respect to movement‐related information, that is, the decoding problem we focus on in this study.”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Ang et al., 2008
Unmixing of global EEG patterns using a number of spatial filters is typically applied to the whole set of relevant electrodes as a basic step in many successful examples of EEG decoding.
“Unmixing of these global patterns using a number of spatial filters is therefore typically applied to the whole set of relevant electrodes as a basic step in many successful examples of EEG decoding [Ang et al., 2008; Blankertz et al., 2008; Rivet et al., 2009].”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Ang et al., 2008
FBCSP was the best-performing method for the BCI competition IV dataset 2a.
“FBCSP was the best‐performing method for the BCI competition IV dataset 2a, which we use in this study”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Ang et al., 2008
For the BCI competition datasets, special care to avoid decoding eye-related signals was requested by the publishers, and a highpass filter was one of the suggested methods to remove eye artefacts; the winners of the competition used this method.
“We included this analysis as for the BCI competition datasets special care to avoid decoding eye‐related signals was requested from the publishers of the datasets [Brunner et al., 2008] and a highpass filter was one of the suggested methods to remove eye artefacts; indeed this was the method the winners of the competition used [Ang et al., 2008].”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Ang et al., 2008
Cites
Elements of Information Theory
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Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works
Pfurtscheller, G., & Aranibar, A. (1979) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(79)90063-4 · Cited by 758 · cited 1× · see citing works
“COMMON SPATIAL PATTERN ALGORITHM The neurophysiological background of motor-imagery based BCIs is that motor activity, both actual and imagined [9],[10], causes an attenuation or increase of localized neural rhythmic activity called Event-Related Desynchronization or Event-Related Synchronization…”
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Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751 · see citing works
BCILAB: a platform for brain–computer interface development
Kothe, C. A., & Makeig, S. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056014 · Cited by 412 · see citing works
Data augmentation for self-paced motor imagery classification with C-LSTM.
Freer, D., & Yang, G.-Z. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab57c0 · Cited by 96 · see citing works
Implementation of a Brain-Computer Interface on a Lower-Limb Exoskeleton
Wang, C., Wu, X., Wang, Z., & Ma, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2853628 · Cited by 89 · see citing works
“CSP algorithm is an effective method to extract the discriminated features of the EEG signal from two classes in the case of motor imagery [23], [24].”
MethodA systematic deep learning model selection for p300-based brain-computer interfaces
Abibullaev, B., & Zollanvari, A. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3051136 · Cited by 53 · see citing works
Subject adaptation convolutional neural network for EEG-based motor imagery classification
Liu, S., Zhang, J., Wang, A., Wu, H., Zhao, Q., & Long, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac9c94 · Cited by 29 · see citing works
Guo, S., Wang, Y., Liu, Y., Zhang, X., & Tang, B. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107756 · Cited by 7 · see citing works
- Authors
- Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Venue
- 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 2390-2397
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2132360759
- Cited by
- 1,514
- References
- 3
- Citations by year
- 2026: 80 · 2025: 186 · 2024: 204 · 2023: 168 · 2022: 149 · 2021: 160 · 2020: 126 · 2019: 102 · 2018: 64 · 2017: 48 · 2016: 48 · 2015: 42 · 2014: 24 · 2013: 25 · 2012: 30
- FWCI
- 37.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Funders
- Institute for Infocomm Research
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- filter bank common spatial pattern, common spatial pattern, brain-computer interface, motor imagery, EEG, spatial filtering, frequency band selection, feature selection, stroke patients, EEG classification
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