Understanding perception of active noise control system through multichannel EEG analysis
In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise...
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doaj-81f840609ab741a6a89d3c8396b744c52021-04-02T11:49:22ZengWileyHealthcare Technology Letters2053-37132018-06-0110.1049/htl.2017.0016HTL.2017.0016Understanding perception of active noise control system through multichannel EEG analysisSangeeta Bagha0R.K. Tripathy1Pranati Nanda2Pranati Nanda3C. Preetam4Debi Prasad Das5CSIR-Institute of Minerals and Materials TechnologyFaculty of Engineering and Technology (ITER)All India Institute of Medical Sciences (AIIMS)All India Institute of Medical Sciences (AIIMS)All India Institute of Medical Sciences (AIIMS)CSIR-Institute of Minerals and Materials TechnologyIn this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ([inline-formula]) and 99.31% ([inline-formula]), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0016electroencephalographyactive noise controldiscrete wavelet transformssingular value decompositionmedical signal processingsignal classificationactive noise control systemmultichannel EEG analysisANCelectroencephalogramsilent listening conditionmusicbackground noisemultiscale analysisdiscrete wavelet transformmultivariate multiscale matricessub-band signalssingular value decompositionmultivariate matricessingular value featuresextreme learning machine classifieractivation functionshuman brain |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sangeeta Bagha R.K. Tripathy Pranati Nanda Pranati Nanda C. Preetam Debi Prasad Das |
spellingShingle |
Sangeeta Bagha R.K. Tripathy Pranati Nanda Pranati Nanda C. Preetam Debi Prasad Das Understanding perception of active noise control system through multichannel EEG analysis Healthcare Technology Letters electroencephalography active noise control discrete wavelet transforms singular value decomposition medical signal processing signal classification active noise control system multichannel EEG analysis ANC electroencephalogram silent listening condition music background noise multiscale analysis discrete wavelet transform multivariate multiscale matrices sub-band signals singular value decomposition multivariate matrices singular value features extreme learning machine classifier activation functions human brain |
author_facet |
Sangeeta Bagha R.K. Tripathy Pranati Nanda Pranati Nanda C. Preetam Debi Prasad Das |
author_sort |
Sangeeta Bagha |
title |
Understanding perception of active noise control system through multichannel EEG analysis |
title_short |
Understanding perception of active noise control system through multichannel EEG analysis |
title_full |
Understanding perception of active noise control system through multichannel EEG analysis |
title_fullStr |
Understanding perception of active noise control system through multichannel EEG analysis |
title_full_unstemmed |
Understanding perception of active noise control system through multichannel EEG analysis |
title_sort |
understanding perception of active noise control system through multichannel eeg analysis |
publisher |
Wiley |
series |
Healthcare Technology Letters |
issn |
2053-3713 |
publishDate |
2018-06-01 |
description |
In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ([inline-formula]) and 99.31% ([inline-formula]), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain. |
topic |
electroencephalography active noise control discrete wavelet transforms singular value decomposition medical signal processing signal classification active noise control system multichannel EEG analysis ANC electroencephalogram silent listening condition music background noise multiscale analysis discrete wavelet transform multivariate multiscale matrices sub-band signals singular value decomposition multivariate matrices singular value features extreme learning machine classifier activation functions human brain |
url |
https://digital-library.theiet.org/content/journals/10.1049/htl.2017.0016 |
work_keys_str_mv |
AT sangeetabagha understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis AT rktripathy understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis AT pranatinanda understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis AT pranatinanda understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis AT cpreetam understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis AT debiprasaddas understandingperceptionofactivenoisecontrolsystemthroughmultichanneleeganalysis |
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