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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Bibliographic Details
Published in:Healthcare technology letters 2018-06, Vol.5 (3), p.101-106
Main Authors: Bagha, Sangeeta, Tripathy, R.K, Nanda, Pranati, Preetam, C, Das, Debi Prasad
Format: Article
Language:English
Subjects:
activation functions
active noise control
active noise control system
ANC
Approximation
background noise
Bands
Brain research
Decomposition
discrete wavelet transform
discrete wavelet transforms
Electrodes
electroencephalogram
Electroencephalography
extreme learning machine classifier
human brain
Listening
medical signal processing
multichannel EEG analysis
multiscale analysis
multivariate matrices
multivariate multiscale matrices
music
Musical performances
Musicians & conductors
Noise control
Physiology
signal classification
Signal processing
silent listening condition
singular value decomposition
singular value features
Sound
Statistical analysis
sub-band signals
Wavelet transforms
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Summary: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% ($p \lt 0.001$p
ISSN:2053-3713
2053-3713
DOI:10.1049/htl.2017.0016