Abstract

This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.

Highlights

  • The integration of artificial intelligence (AI) techniques with wearable robots has been used in human applications, such as bionic hands and prosthetic orthosis

  • Lag and dimensions are presented, which are the initial values for Mutual Information (MI) and False Nearest Neighbor (FNN), respectively

  • In order to determine the effects of the optimization algorithms on the largest Lyapunov exponent (LLE) and phase space reconstruction, the figures of trajectories based on Water Drop (WD) (Figures 6, 7) and Chaotic Tug of War (CTW) (Figures 8, 9) are illustrated

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Summary

Introduction

The integration of artificial intelligence (AI) techniques with wearable robots has been used in human applications, such as bionic hands and prosthetic orthosis. We used electroencephalograph (EEG) signal processing algorithms for the control of a bionic hand as a new design. We focused on an automatic identification method based on the largest Lyapunov exponent (LLE) chaotic feature to control a bionic hand robot. Depending on the complexity of the signal and the introduced mother wavelet, the obtained results are most significant, but a critical limitation exists: the computations are very time consuming. The other useful feature is LLE [5], which is widely used in different fields of studies such as schizophrenia [6, 7], sleep EEG processing and memory investigations, BCI applications for control of remote vehicles [4], as well as in bionic hands, and for the prediction of epilepsy seizure attacks [8, 9]

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