Discrimination of four class simple limb motor imagery movements for brain–computer interface

• Published in: Biomedical signal processing and control Vol. 44; pp. 181 - 190
• Main Authors: Abdalsalam M, Eltaf, Yusoff, Mohd Zuki, Mahmoud, Dalia, Malik, Aamir Saeed, Bahloul, Mohammad Rida
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2018
• Subjects: Artificial neural network (ANN); Brain computer interface (BCI); Discrete wavelet transform (DWT); Electroencephalography (EEG); Empirical mode decomposition (EMD); Discrete wavelet transform (DWT); Artificial neural network (ANN); Electroencephalography (EEG); Empirical mode decomposition (EMD); Brain computer interface (BCI)
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2018.04.010

•The study developed extraction methods for tasks involving imagination of right hand, left hand, right foot, or left foot.•ANN was used in “DWT + ANN” and “EMD + ANN” to test DWT and EMD efficiency, using energy, entropy, and absolute power.•In offline analysis, EMD discriminates imagination of right and left feet more clearly than DWT, although same Cz was used.•We propose EMD as a promising feature extractor since it outperforms DWT in many discrimination undertaking cases.•Future work will use EMD for online processing, task discrimination, and will solve inter- and intra-subject variability. The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in significant areas including BCI, solutions need to be formulated to overcome the task discrimination issues faced when a motor imagery movement approach is utilized. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose four-class movement imaginations of the right hand, left hand, right foot, and left foot, and develop feature extraction methods utilizing discrete wavelet transform (DWT) and empirical mode decomposition (EMD); in both methods, artificial neural network (ANN) was used as a classifier. Based on the processed electroencephalography (EEG) data recorded from eleven subjects, it can be seen that EMD features outperform DWT features; the average accuracy achieved by the EMD features is 90.02%, and 84.77% using the DWT features. EMD even performs better than DWT in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex.