Towards Classifying Motor Imagery Using a Consumer-Grade Brain-Computer Interface

This research attempts to classify electroencephalogram (EEG) signals of motor imagery of left and right hand movement with a consumer-grade brain-computer interface device, which consists of four channels. For this purpose, we designed an interface to collect a total of approximately 600 samples for left and right hand motor imagery from two subjects. Hilbert-Huang Transform was used for feature extraction, and we applied support-vector machine (SVM) and k-nearest neighbors (k-NN) algorithms for learning the features and classification. Results show that these methods have some ability to classify left and right hand motor imagery EEG signals. This paper outlines the used methodology which could be a reference for future studies of the same nature.

[1]  Cuntai Guan,et al.  Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.

[2]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[3]  Yijun Wang,et al.  Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[4]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.

[5]  Sadasivan Puthusserypady,et al.  An end-to-end deep learning approach to MI-EEG signal classification for BCIs , 2018, Expert Syst. Appl..

[6]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  Aamir Saeed Malik,et al.  Comparison of EEG signal decomposition methods in classification of motor-imagery BCI , 2018, Multimedia Tools and Applications.

[8]  Mohamed Jemni,et al.  A feature extraction technique of EEG based on EMD-BP for motor imagery classification in BCI , 2015, 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA).

[9]  Cheolsoo Park,et al.  Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.