The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Classifying Single-Trial EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL)
Abstract
In most current motor-imagery-based brain-computer interfaces (BCIs), machine learning is carried out in two consecutive stages: feature extraction and feature classification. Feature extraction has focused on automatic learning of spatial filters, with little or no attention being paid to optimization of parameters for temporal filters that still require time-consuming, ad hoc manual tuning. In this paper, we present a new algorithm termed iterative spatio-spectral patterns learning (ISSPL) that employs statistical learning theory to perform automatic learning of spatio-spectral filters. In ISSPL, spectral filters and the classifier are simultaneously parameterized for optimization to achieve good generalization performance. A detailed derivation and theoretical analysis of ISSPL are given. Experimental results on two datasets show that the proposed algorithm can correctly identify the discriminative frequency bands, demonstrating the algorithm's superiority over contemporary approaches in classification performance.
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Citations
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 1×
“A highly successful spatial filtering algorithm, as evidenced by BCI Competition II and III [5], [7], is common spatial patterns (CSPs) (also known in the machine learning field as the Fukunaga–Koontz transform), which was first proposed as an application of the Karhunen–Lo` eve transform for classification by Fukunag…”
MethodMotor imagery classification by means of source analysis for brain–computer interface applications
Qin, L., Ding, L., & He, B. (2004) · Journal of Neural Engineering · doi:10.1088/1741-2560/1/3/002 · Cited by 239
BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation
McFarland, D. J., Anderson, C. W., Muller, K.-R., Schlogl, A., & Krusienski, D. J. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875637 · Cited by 204
Cited by
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00039 · Cited by 1,264
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks
Sakhavi, S., Guan, C., & Yan, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2789927 · Cited by 656
Emotion classification based on gamma-band EEG
Li, M., & Lu, B.-L. (2009) · 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2009.5334139 · Cited by 375
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
Lu, H., Eng, H.-L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2082540 · Cited by 309
Metadata
- Authors
- Wu, W., Gao, X., Hong, B., & Gao, S.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-06
- Volume
- 55
- Issue
- 6
- Pages
- 1733-1743
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2147174595
- Cited by
- 179
- References
- 4
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- closed
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