Movement intention detection using neural network for quadriplegic assistive machine

Abstract

Biomedical signal lately have been a hot topic for researchers, as many journals and books related to it have been publish. In this paper, the control strategy to help quadriplegic patient using Brain Computer Interface (BCI) on basis of Electroencephalography (EEG) signal was used. BCI is a technology that obtain user's thought to control a machine or device. This technology has enabled people with quadriplegia or in other words a person who had lost the capability of his four limbs to move by himself again. Within the past years, many researchers have come out with a new method and investigation to develop a machine that can fulfill the objective for quadriplegic patient to move again. Besides that, due to the development of bio-medical and healthcare application, there are several ways that can be used to extract signal from the brain. One of them is by using EEG signal. This research is carried out in order to detect the brain signal to controlling the movement of the wheelchair by using a single channel EEG headset. A group of 5 healthy people was chosen in order to determine performance of the machine during dynamic focusing activity such as the intention to move a wheelchair and stopping it. A neural network classifier was then used to classify the signal based on major EEG signal ranges. As a conclusion, a good neural network configuration and a decent method of extracting EEG signal will lead to give a command to control robotic wheelchair.

Controlling a Wheelchair Using Human-Computer Interaction

“…Another way for designing such systems is to use brain signals, which express user's thought. The authors in [5] proposed a system that extracts signals from the brain using Electroencephalography (EEG). EEG is the basis for Brain Computer Interface (BCI), which extracts the user's brain signals that reflect his wishes to drive the machine.…”

Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

“…The classification process is very useful for analyzing brain pattern characteristics and interpreting EEG signal features represented in a high-dimensional feature space [32]. Numerous machine learning algorithms the BCW literature, including support vector machines (SVMs) [2,14,[33][34][35][36][37][38][39][40][41][42][43][44][45][46], linear discriminant analysis (LDA) [47][48][49][50][51][52][53][54][55][56][57], decision trees (DTs) [5,6,58,59], K-nearest neighbors (KNNs) [60,61], naive bases (NBs) [43,60,62,63], logistic regression (LR) [1,64], and artificial neural networks (ANNs) [1,45,60,61,[64][65][66][67]…”

Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization

“…Recently, bioelectrical signal has gained popularity among several prominent research institutes. 5,6,[15][16][17][18] Aside from the raise of the IoT devices, other breakthroughs in the related fields such as cardiology, muscle physiology, and neuroscience also help improving the EMG field. Availability of low-cost sensors and semiconductors, combined with the increasingly deeper knowledge about the subject of muscle and human gesture, has propelled the research about EMG and its implementation outside the medical field.…”