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A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals

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Abstract

In this study, two-channel surface electromyography (sEMG) signals were used to classify hand finger movements. Bicoherence analysis of the sEMG signal recorded with surface electrodes for flexor and extensor muscle bundles on the front and back of the forearm, respectively, was classified by extreme learning machines (ELM) based on phase matches in the electromyography (EMG) signal. EMG signals belonging to 42 human, 22 males and 20 females, with an average age of 21.4 were used in the study. The finger movements were also classified by using different learning algorithms. The best classification was performed by using ELM algorithm with 98.95 and 97.83% accuracies in average for subjects individually and all together, respectively. On the other hand, a maximum of 95.81 and 94.30% accuracies were reached for subjects individually and all together, respectively, with other learning methods used in the present study. From the information obtained through this study, it is possible to control finger movements by using flexor and extensor muscle activities of the forearm. Furthermore, by this method, it may be possible controlling of the intelligent prosthesis hands with high degree of freedom.

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  1. Department of Electrical and Electronics Engineering, Batman University, Batman, Turkey

    Necmettin Sezgin

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Sezgin, N. A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals. Neural Comput & Applic 31, 3327–3337 (2019). https://doi.org/10.1007/s00521-017-3286-z

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