Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System

Abstract

Brain-computer interface (BCI) is a system for communication and control between the human brain and computers or other electronic devices. However, multichannel signal acquisition, which is timeconsuming, laborious, and not conducive to subsequent real-time signal processing, can cause channel redundancy. When designing a BCI system, the selection of the optimal channels that match the expected pattern of potential cortical activity is useful for classifying brain activity during a mental task. The Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of channel selection to extract the most relevant channel for classification. The superior performance of the algorithm is demonstrated by the test results on BCI Competition III dataset I. For a motor imagery paradigm, we show that the number of used channels can be reduced significantly, and the classification performance of the classifier can be improved. By comparing the performances of the algorithm with or without channel selection, the best channel combination was selected, and only 28 out of the 64 acquisition electrodes were used to realize classification sensitivity, specificity, precision, accuracy and Kappa coefficient values of 98%, 96%, 96.08%, 97% and 0.94, respectively, thereby outperforming existing algorithms. The proposed algorithm can reduce the number of channels and select the best channel combination to improve the classification performance, thereby overcoming the redundant channel problem. In addition, the signal processing framework that is adopted by this research can serve as a reference for related BCI application system research.

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

“…Chang & Yang, (2019) [17] applied Stockwell transform and Bayesian linear discriminant analysis in feature generation and classification, respectively, and a Genetic Algorithm (GA) in the channel selection process to find optimal channel subsets. The algorithm was demonstrated on the BCI Competition III dataset I and realizes 98% classification accuracy with a 96% sensitivity score.…”

Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network

“…Otak manusia merupakan sistem komputasi cerdas alami yang memiliki kemampuan untuk mengendalikan semua aktivitas saraf manusia seperti perilaku, pikiran, dan emosi termasuk pergerakan tubuh [5]. Brain-Computer Interface (BCI) berbasis sinyal EEG merupakan sistem yang menerjemahkan pola aktivitas otak pengguna menjadi pesan atau perintah sehingga dapat digunakan untuk mengendalikan komputer dan perangkat-perangkat eksternal [6]. BCI merupakan perangkat komunikasi yang menghubungkan otak manusia secara langsung kepada perangkat eksternal tanpa melibatkan sistem syaraf tepi dan jaringan otot [6].…”

“…Brain-Computer Interface (BCI) berbasis sinyal EEG merupakan sistem yang menerjemahkan pola aktivitas otak pengguna menjadi pesan atau perintah sehingga dapat digunakan untuk mengendalikan komputer dan perangkat-perangkat eksternal [6]. BCI merupakan perangkat komunikasi yang menghubungkan otak manusia secara langsung kepada perangkat eksternal tanpa melibatkan sistem syaraf tepi dan jaringan otot [6]. Perkembangan terbaru dalam bidang organ buatan berbasis BCI yang memungkinkan pengendalian perangkat eksternal melalui otak secara langsung, memberikan kesempatan peningkatan taraf hidup penyandang disabilitas khususnya bagi pasien yang mengalami gangguan fungsi organ gerak [7].…”

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

“…[7]. In terms of neurophysiology, motor imagery accompanies attenuation or enhancement of rhythmical synchrony over the sensorimotor cortex with the frequency bands of alpha (8)(9)(10)(11)(12)(13) and beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [8] - [11]. This paper focuses on EEG based classification of two motor imagery tasks.…”

“…A regularized CSP (RCSP) method is used to extract effective features to perform the MI classification. The genetic algorithm (GA) is also used for channel selection to extract the most relevant channels for enhanced classification [22]. The distinctive channels in terms of correlation coefficient (CC) values are selected to enhance the classification performance [23].…”