An Automatic Subject Specific Intrinsic Mode Function Selection for Enhancing Two-Class EEG-Based Motor Imagery-Brain Computer Interface

• Published in: IEEE sensors journal Vol. 19; no. 16; pp. 6938 - 6947
• Main Authors: Gaur, Pramod, Pachori, Ram Bilas, Wang, Hui, Prasad, Girijesh
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandpass filters; Basis functions; BCI; Channels; common spatial pattern; Discrete Wavelet Transform; Discriminant analysis; Electroencephalography; Empirical analysis; Emulation; Error detection; Feature extraction; filtering; Fourier transforms; Frequency analysis; Frequency-domain analysis; Human-computer interface; Image classification; Image enhancement; Image reconstruction; linear discriminant analysis; MEMD; Signal to noise ratio; Support vector machines; Task analysis; Wavelet transforms
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2912790

The electroencephalogram (EEG) 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 EEG 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 EEG 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 % <inline-formula> <tex-math notation="LaTeX">(p = 0.0078) </tex-math></inline-formula> with raw EEG signals, >13% <inline-formula> <tex-math notation="LaTeX">(p = 0.0039) </tex-math></inline-formula> with empirical mode decomposition-based filtering and >17 % <inline-formula> <tex-math notation="LaTeX">(p = 0.0039) </tex-math></inline-formula> with discrete wavelet transform-based filtering.