Journal
IEEE SENSORS JOURNAL
Volume 19, Issue 16, Pages 6938-6947Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2019.2912790
Keywords
BCI; MEMD; filtering; common spatial pattern; linear discriminant analysis
Funding
- Ulster University Vice Chancellor's Research Scholarship (VCRS)
- Northern Ireland Functional Brain Mapping Facility [1303/101154803]
- UKIERI DST Thematic Partnership [DST-UKIERI-2016-17-0128]
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The electroencephalogram (EEC) signals tend to have poor time-frequency localization when analysis techniques involve a fixed set of basis functions such as in short-time Fourier transform and wavelet transform. These signals also exhibit highly non-stationary characteristics and suffer from low signal-to-noise ratio (SNR). As a result, there is often poor task detection accuracy and high error rates in designed brain-computer interfacing (BCI) systems. In this paper, a novel preprocessing method is proposed to automatically reconstruct the EEG signal by selecting the intrinsic mode functions (IMFs) based on a median frequency measure. Multivariate empirical mode decomposition is used to decompose the EEC signals into a set of IMFs. The reconstructed EEG signal has high SNR and contains only information correlated to a specific motor imagery task. The common spatial pattern is used to extract features from the reconstructed EEC signals. The linear discriminant analysis and support vector machine have been utilized in order to classify the features into left hand motor imagery and right hand motor imagery tasks. Our experimental results on the BCI competition IV dataset 2A show that the proposed method with fifteen channels outperforms bandpass filtering with 22 channels (> 1%) and by > 9 % (p = 0.0078) with raw EEG signals, > 13% (p = 0.0039) with empirical mode decomposition-based filtering and > 17 % ( p = 0.0039) with discrete wavelet transform-based filtering.
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