Abstract
The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.
Highlights
Brain-computer interface (BCI) is defined as an alternative communication pathway which translates the measured brain activity into control commands (Pfurtscheller et al, 2008)
As the EEG α band is of interest, the frequency range for band-limited multiple Fourier linear combiner (BMFLC)-KF is set to f1 = 6 Hz and fn = 14 Hz. f is set to 0.5 Hz as it has been shown to offer better results in EEG signal decomposition (Wang et al, 2012b)
The performance of BMFLC-KF with various combinations of filter bank common spatial pattern (FBCSP),common spatial filter (CSP), covariance matrix adaptation evolution strategy (CMA-ES), and global and local real-coded genetic algorithm (GLGA) for feature optimization is quantified with a two-class BCI application
Summary
Brain-computer interface (BCI) is defined as an alternative communication pathway which translates the measured brain activity into control commands (Pfurtscheller et al, 2008). Among the existing brain activity measurement techniques, EEG has been extensively employed for BCI applications due to its non-invasiveness, ease of implementation, and cost-efficiency (Lotte et al, 2007). The motor imagery has been one of the successful methods for EEG based BCI systems (McFarland et al, 2010; Hill et al, 2014). The application of motor imagery task to BCI systems has enabled subjects to sufficiently control a moving cursor in 2D space or a quadcopter in 3D space (Wolpaw et al, 2004; LaFleur et al, 2013). The motor imagery based BCI systems require transforming the recorded EEG signal into frequency domain in quasi real-time. The extracted frequency domain features are fed to machine learning algorithms for classification (Lotte et al, 2007; Robinson et al, 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
