A Review on Electroencephalogram Based Brain Computer Interface for Elderly Disabled

Wan, Xin; Zhang, Kezhong; Ramkumar, S.; Deny, J.; Emayavaramban, G.; Ramkumar, M. Siva; Hussein, Ahmed Faeq

doi:10.1109/access.2019.2903235

Cited by 70 publications

(19 citation statements)

References 32 publications

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“…A. Feature Extraction Subject selection, Experimental setup, Protocol design and signal collection methods were already prescribed in our preceding study [29]- [32]. An EEG signal gathered from subject2 during data collection was illustrated in the fig.…”

Section: Methodsmentioning

confidence: 99%

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

2019

IJRTE

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Brain Computer Interface (BCI) using Electrooencephalogram (EEG) is one of the versatile tools to measure the brain thoughts and convert them to operate the external devices in the deficiency of biological controls. These techniques are used to develop the rehabilitative devices for the individual person who affected with locked in syndrome (LIS). The main reason for LIS is due to death of motor neurons. To overcome the real world problem we conduct our experiment with eight normal patients for four tasks using three electrode system and ADI T26 bio amplifier with Lab chart. Four task signals are applied to statistical method to retrieve the twenty two features and trained with the feed forward neural network (FFNN) and feed forward neural network with wolf Grey optimization algorithm (FFNNWGOA) to see network model which was more perfectly supported to identify the tasks. The study showed that statistical features through feed forward neural network with grey wolf optimized algorithm classifier produced maximum performances of 94.06% compared to other feed forward neural network classifier model and also we identified that optimized features demonstrated maximum performances in minimum time duration during training in all the following twenty trials.

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Section: Methodsmentioning

confidence: 99%

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

2019

IJRTE

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“…To standardize positions and naming, electrodes are placed on the scalp under international 10–20 standard. This helps in reliable data collection and consistency among different BCI sessions [ 10 ].…”

Section: Architecture Of 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

103

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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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“…where W j denotes the N×P/2 j array wavelet transform matrix at the jth level obtained by applying the P×P/2 j orthonormal wavelet transformation matrix W to the snapshot of the N channels array data matrix X=[x (1) ,x (2) ,…,x (N) ] [35]. The Eigen vector is chosen corresponding to the maximum Eigen value obtained from Eigen decomposition of array wavelet covariance matrix.…”

Section: A Multiband Decompositionmentioning

confidence: 99%

“…It is a tool which offers a communication channel and control capabilities that allow direct connection between human brain and external device using brain activities [1]. The technology used in BCI translates human intention into command which cannot pass through the brain's normal output pathway of peripheral nerves and muscles [2]. Recently, several attempts are reported to achieve the goal [3] of BCI.…”

Section: Introductionmentioning

confidence: 99%

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

et al. 2021

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A Review on Electroencephalogram Based Brain Computer Interface for Elderly Disabled

Cited by 70 publications

References 32 publications

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

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

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

Trial Regeneration With Subband Signals for Motor Imagery Classification in BCI Paradigm

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