EEG electrode selection method based on BPSO with channel impact factor for acquisition of significant brain signal

Park, Seung Min; Kim, Jun-Yeup; Sim, Kwee-Bo

doi:10.1016/j.ijleo.2017.10.085

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…This method has shown good improvement in classification accuracy even in session to session and subject to subject transfer MI-BCI scenarios. Park et al [ 101 ] applied particle swarn optimization algorithm to find subject specific optimal number of electrodes. These electrodes’ EEG data is further used for classification.…”

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%

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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“…Moctezuma et al applied the non-dominated sorting genetic algorithms (NSGA) to select EEG channels [28]. Park et al compared channel selection results based on binary PSO (BPSO), BPSO with a channel impact factor, a genetic algorithm (GA) and harmony search (HS) [29]. Most of these researchers focused the channel selection on an optimization problem rather than revealing the relationship between features and EEG channels.…”

Section: Introductionmentioning

confidence: 99%

Sensitive Channel Selection for Mental Workload Classification

Jin

Qu²,

Pang³

et al. 2022

Mathematics

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Mental workload (MW) assessment has been widely studied in various human–machine interaction tasks. The existing researches on MW classification mostly use non-invasive electroencephalography (EEG) caps to collect EEG signals and identify MW levels. However, the activation region of the brain stimulated by MW tasks is not the same for every subject. It may be inappropriate to use EEG signals from all electrode channels to identify MW. In this paper, an EEG rhythm energy heatmap is first established to visually show the change trends in the energy of four EEG rhythms with time, EEG channels and MW levels. It can be concluded from the presented heatmaps that this change trend varies with subjects, rhythms and channels. Based on the analysis, a double threshold method is proposed to select sensitive channels for MW assessment. The EEG signals of personalized selected channels, named positive sensitive channels (PSCs) and negative sensitive channels (NSCs), are used for MW classification using the Support Vector Machine (SVM) algorithm. The results show that the selection of personalized sensitive channels generally contributes to improving the performance of MW classification.

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“…The purposes of channel selection are reducing computational complexity of any processing task with EEG signals and selecting the relevant channels. [1][2].…”

Section: Introductionmentioning

confidence: 99%

Eeg Channel Selection Using Differantial Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

Şeker¹,

Özerdem²

2021

European Journal of Technic

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EEG electrode selection method based on BPSO with channel impact factor for acquisition of significant brain signal

Cited by 12 publications

References 14 publications

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

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

Sensitive Channel Selection for Mental Workload Classification

Eeg Channel Selection Using Differantial Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

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