Brain-computer interface: controlling a robotic arm using facial expressions

Abstract

Abstract:The aim of this paper is to develop a brain-computer interface (BCI) system that can control a robotic arm using EEG signals generated by facial expressions. The EEG signals are acquired using a neurosignal acquisition headset. The robotic arm consists of a 3-D printed prosthetic hand that is attached to a forearm and elbow made of craft wood. The arm is designed to make four moves. Each move is controlled by one facial expression. Hence, four different EEG signals are used in this work. The performance of the BCI robotic arm is evaluated by testing it on 10 subjects. Initially 14 electrodes were used to collect the EEG signals, and the accuracy of the system is around 95%. We have further analyzed the minimum requirement for the number of electrodes for the system to function properly. Seven (instead of 14) electrodes in the parietal, temporal, and frontal regions are sufficient for the system to function properly.The accuracy of the system with 7 electrodes is around 95%.

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network

“…During this attack remaining cognitive functions are remain unaffected [1]. To overcome such condition some of the remarkable research products developed by using EEG signals were stated here they are, Gaming controller [2], Smart Home Controller [3], TV channel Controller [4], wheelchair [5]- [9], Keyboard controller [10]- [12], Eye tracking Glass [13], Music Player [14]- [15] , Web browser Controller [16], Arm Movement Controller [17]- [19], Mouse Controller [20], Virtual Car Driving System [21]. In this research work we compared and studied the performances of traditional method with hybrid technique using FFNN (with benchmark Levenberg Marquardt back propagation algorithm and hybrid Grey wolf Optimization algorithm).…”