Authors: M.K.M. Rahman; Md. Omer Sadek Bhuiyan; Md. A. Mannan Joadder
Addresses: Department of EEE, United International University, Dhaka-1212, Bangladesh ' Department of EEE, United International University, Dhaka-1212, Bangladesh ' Department of EEE, United International University, Dhaka-1212, Bangladesh
Abstract: Brain-computer interface (BCI) is the most popular research topic to the researchers of neuroprosthetics. The ultimate goal of this research is to develop a communication channel between human brains and external devices. Feature extraction is one of the most crucial steps in this research. Combination of different features may improve the classification performance, but in most of the cases straight forward combinations of different features lead to a very poor result. So, it is necessary to combine the orthogonal features and omit the redundant ones. It is a complex and time-consuming process. We have developed two new algorithms to find optimum sets of features for fusion to obtain best possible classification accuracy for both subject-specific (SS) and subject-independent (SI) cases. Experimental results indicate that our proposed algorithms, in general, improve the classification results irrespective of the different methodological setup of BCI processes such as number of input channels and spatial filter.
Keywords: brain-computer interface; BCI; feature fusion; feature extraction; optimum feature set; EEG; classification of motor imagery; incremental progressive feature fusion; IPFF; decremental progressive feature fusion; DPFF.
International Journal of Biomedical Engineering and Technology, 2021 Vol.36 No.4, pp.375 - 392
Received: 16 Jan 2018
Accepted: 07 Jun 2018
Published online: 02 Aug 2021 *