Spatio-spectral filters for improving the classification of single trial EEG
- Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements
Author details: | Steven Lemm, Benjamin BlankertzGND, Gabriel Curio, Klaus-Robert Müller |
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ISSN: | 0018-9294 |
Publication type: | Article |
Language: | English |
Year of first publication: | 2005 |
Publication year: | 2005 |
Release date: | 2017/03/24 |
Source: | Ieee Transactions on Biomedical Engineering. - ISSN 0018-9294. - 52 (2005), 9, S. 1541 - 1548 |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
Peer review: | Referiert |
Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |