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Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification

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Abstract

Electroencephalogram (EEG) signals show the electrical activity of the brain, which are one of the inputs of the brain–computer interface (BCI). The BCI provides the communication path between the brain and the computer. One of the critical applications of BCI is Motor imagery (MI). MI is a mental process that a person practices or simulates a particular movement without physically acting. BCI allows the person to communicate with their environment independently of peripheral muscles and nerves, using EEG brain signals by assistive devices such as wheelchairs, robotic arms, and computers. In this paper, a space learning concept is proposed for EEG motor imagery signal classification. Our innovation in the proposed method is to increase and then, reduce the data dimensions, which has led to learning the efficient space for signals classification. It is based on two techniques: Multi-Kernel Learning (MKL) and dimension reduction. The composite kernel is made of a combination of four kernels by The Heuristic MKL Algorithm. This algorithm uses heuristic rules to estimate the weight of kernels with high accuracy and very little computational complexity. The weight associated with each base kernel and its parameters is calculated by the Equilibrium Optimizer. Dimensions of data are reduced to avoid the curse of dimensions. In this step, the number of dimensions of reduced space and the mapping matrix are learned to reduce the dimensions of data linearly. We selected ELM, KNN, and SVM classifiers for classification. The BCI Competition dataset was used for evaluation, which consists of five subsets aa, al, ay, aw, av, and two classes of the right hand and right foot. The proposed method with the ELM was improved the average classification accuracy and standard deviation by 3.9% and 2.28, respectively, and achieved 91.4% accuracy. The lower standard deviation than other methods shows that our method is more robust than all other methods to subject variety. The proposed method is compared with twelve state-of-the-art methods and has shown higher accuracy than other methods such as the deep convolutional neural networks. The results show the superiority of the proposed method over other methods in the Wilcoxon signed test.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Youkabed Amiri & Hesam Omranpour

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  1. Youkabed Amiri
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  2. Hesam Omranpour
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YA contributed to programmer validation, visualization, investigation, and writing—original draft. HO contributed to supervision, project administration, conceptualization, methodology, and writing—reviewing and editing.

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Correspondence to Hesam Omranpour.

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Amiri, Y., Omranpour, H. Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification. Neural Comput & Applic 36, 1199–1214 (2024). https://doi.org/10.1007/s00521-023-09090-y

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