Abstract:
The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-...Show MoreMetadata
Abstract:
The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML), electrooculography (EOG) artifact removal method to remove eye-related artifacts to significantly improve the detection performance. We have applied this technique to our locally recorded Emotiv® data set of 6 healthy subjects, where an average detection selectivity of 85±6% and sensitivity of 88±7.7% is achieved with a temporal precision in the range of −1250 to 367 ms in onset detections of single-trials.
Published in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 25-29 August 2015
Date Added to IEEE Xplore: 05 November 2015
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PubMed ID: 26736657