Fruit Fly Optimization based EEG Channel Selection Method for BCI

Yu, Xinyang; Yu, Jong Min; Sim, Kwee-Bo

doi:10.5302/j.icros.2016.14.0075

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Key Issues In MI Based Bci1

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2021

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“…This significantly improves the accuracy compared to traditional methods. Yu et al [ 103 ] used Fly optimization algorithm (FOA) to select the best channel for subject and then extracted CSP features from these channels for the classification. They also compared FOA performance with GA and PSO.…”

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

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Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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Section: Key Issues In MI Based Bcimentioning

confidence: 99%