Recognition of Flexion and Extension Imagery Involving the Right and Left Arms Based on Deep Belief Network and Functional Near-Infrared Spectroscopy.

Yunfa Fu,Lei Zhao,Anmin Gong,Rui Chen,Qian Qian,Ning Ding,Wei Zhang,Lei Su,Jinshan Tang

doi:10.1155/2021/5533565

Abstract

Brain-computer interaction based on motor imagery (MI) is an important brain-computer interface (BCI). Most methods for MI classification are based on electroencephalogram (EEG), and few studies have investigated signal processing based on MI-Functional Near-Infrared Spectroscopy (fNIRS). In addition, there is a need to improve the classification accuracy for MI fNIRS methods. In this study, a deep belief network (DBN) based on a restricted Boltzmann machine (RBM) was used to classify fNIRS signals of flexion and extension imagery involving the left and right arms. fNIRS signals from 16 channels covering the motor cortex area were recorded for each of 10 subjects executing or imagining flexion and extension involving the left and right arms. Oxygenated hemoglobin (HbO) concentration was used as a feature to train two RBMs that were subsequently stacked with an additional softmax regression output layer to construct DBN. We also explored the DBN model classification accuracy for the test dataset from one subject using training dataset from other subjects. The average DBN classification accuracy for flexion and extension movement and imagery involving the left and right arms was 84.35 ± 3.86% and 78.19 ± 3.73%, respectively. For a given DBN model, better classification results are obtained for test datasets for a given subject when the model is trained using dataset from the same subject than when the model is trained using datasets from other subjects. The results show that the DBN algorithm can effectively identify flexion and extension imagery involving the right and left arms using fNIRS. This study is expected to serve as a reference for constructing online MI-BCI systems based on DBN and fNIRS.

Highlights

In order to improve the classification accuracy for braincomputer interfaces (BCIs), a deep belief network (DBN) was designed to classify functional near-infrared spectroscopy signals. is paper used oxygenated hemoglobin (HbO) concentration as a feature to train two restricted Boltzmann machines (RBMs) which were subsequently stacked with an additional softmax regression output layer to construct a DBN. e main goal of brain-computer interface (BCI) is to bypass peripheral nerves and muscles to establish direct communication and control between the brain and the outside world
Data obtained from each trial for the 10 subjects were extracted and averaged according to the four defined types of tasks. e average functional near-infrared spectroscopy (fNIRS) response obtained for flexion and extension movement or imagery involving the right and left arms is shown in Figures 5(a) and 5(b)
We explored the feasibility of the recognition of flexion and extension movement or imagery involving the right and left arms using DBN and fNIRS. e proposed method achieves a higher classification accuracy (78.19 ± 3.73%) for two types of Motor imagery (MI) fNIRS signals based on DBN compared to the traditional algorithms adopted by Sitaram et al [27], Naseer and Hong [29], and Zhang et al [38], indicating the effectiveness of the DBN algorithm used in this paper

Summary

Introduction

In order to improve the classification accuracy for braincomputer interfaces (BCIs), a deep belief network (DBN) was designed to classify functional near-infrared spectroscopy (fNIRS) signals. is paper used oxygenated hemoglobin (HbO) concentration as a feature to train two restricted Boltzmann machines (RBMs) which were subsequently stacked with an additional softmax regression output layer to construct a DBN. e main goal of BCI is to bypass peripheral nerves and muscles to establish direct communication and control between the brain and the outside world. It uses changes in light intensity to calculate oxygenated and deoxygenated hemoglobin concentrations, and functional neurological activity is indirectly inferred from this metabolic activity [6,7,8]. Most methods for MI classification are based on electroencephalogram (EEG), while less attention has been paid to fNIRS. One of its advantages is its ability to tolerate a certain degree of movement of the subject’s head It is highly portable and suitable for monitoring dynamic changes of oxygenation and deoxygenated hemoglobin concentration in brain tissue during movement and imagining; it has been applied successfully in many fields [14,15,16,17,18,19,20,21,22,23]. Studies have shown that MIBCI based on fNIRS (fNIRS-MI-BCI) is feasible and has several potential applications [24,25,26]

Objectives

Methods

Results

Discussion

Conclusion