Paper Info
Title
Efficient Classification of Motor Imagery using Particle Swarm Optimization-based Neural Network for IoT Applications
Abstract
The Internet of Things (IoT) has found wide applications in human endeavors, and has recently drawn massive attention in the field of cognitive science by connecting the Brain-Computer Interface (BCI) to the cloud. The integration of EEG-based BCI with internet is rapidly developing due to its non-invasiveness, and the benefits are inexhaustible such as control of assistive robots, telemedicine and telehealth for secured monitoring. However, some challenges exist which would prevent the application of BCIs, including the accurately predicting movement intention from EEG signals, computational complexity while preprocessing EEG tasks, and the problem of overfitting. Several algorithms have been used to address these challenges, but they are still limited to be applied efficiently on the BCI-based IoT. In this paper, in order to tackle the above mentioned issues, we proposed an extended Particle Swarm Optimization (PSO)-based neural network (NN) which is able to provide a seamless interaction between the BCI and IoT devices. In the experiments, we designed a BCI system that captured relevant EEG information by means of the PSO, and then projected the features into a neural network system for training. The experimental results demonstrated the feasibility of the proposed PSO-based NN technique in classifying the motor imagery (MI) tasks with an accuracy of 98.9%. Some possible improvement in future work was also suggested.
Year
DOI
Venue
2020
10.1109/MetroInd4.0IoT48571.2020.9138229
2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT
Keywords
DocType
ISBN
EEG-based BCI,MI,IoT,PSO_NN
Conference
978-1-7281-4892-2
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Oluwagbenga Paul Idowu142.77
O. W. Samuel216122.87
Xiangxin Li3458.34
Mojisola Grace Asogbon400.34
Peng Fang53015.63
Guanglin Li631457.23