Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study

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.

EEG-Based Classification of Spoken Words Using Machine Learning Approaches

“…The SVM is a binary classifier; however, it can be extended by merging several types into a multiclass classifier by implementing the one-versus-one approach [40]. SVM is an algorithm that has shown effective results in EEG signal applications [14,15,22,23], and it is also one of the most popular machine learning algorithms used to decode speech from EEG signals [41]. Therefore, we define the method that uses feature extraction based on power spectral density, and for classification, it employs support vector machines, as the PSD + SV M method.…”

“…In MI, the participant imagines moving a limb without actual movement; these signals are then decoded, and a command is sent to activate an external device [10]. While these paradigms have been widely used in a range of BCI applications [11][12][13][14][15], they are limited when the primary requirement for the user is direct communication through speech rather than control of movement. This is especially relevant for conditions such as bulbar ALS, dysarthria, anarthria, stroke, and Parkinson's disease, among other speech disorders.…”

Neural Encoding of Pavement Textures during Exoskeleton Control: A Pilot Study

“…Considering the possibility of diminished differences in somatosensory neural activity due to the aforementioned factors, it is important to evaluate the following: (a) the general patterns of neural activity associated with exoskeleton use and (b) the performance of common classifiers decoding the neural activity of individuals controlling an exoskeleton when encountering different textured pavements. Neural correlates of tactile sensory processing have been extensively studied using electroencephalography [4][5][6][7], but there has been a growing interest in complementing the study of neural signals with the use of classifiers [8][9][10][11][12][13][14][15]. Some of the most popular classifiers for neural decoding include the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN) [12][13][14].…”

“…Neural correlates of tactile sensory processing have been extensively studied using electroencephalography [4][5][6][7], but there has been a growing interest in complementing the study of neural signals with the use of classifiers [8][9][10][11][12][13][14][15]. Some of the most popular classifiers for neural decoding include the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN) [12][13][14].…”

Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report

“…This is an important finding providing a basis for the popularity of the motor imagery BCI (MI BCI) applications in functional recovery in post-stroke patients. Nevertheless, the dependence of the MI BCI performance on the patient's concentration during MI execution is a challenging problem in this area [9][10][11][12][13][14].…”