RT Journal Article
A1 Sangeeta Bagha
A1 R.K. Tripathy
A1 Pranati Nanda
A1 C. Preetam
A1 Debi Prasad Das

PB iet
T1 Understanding perception of active noise control system through multichannel EEG analysis
JN Healthcare Technology Letters
VO 5
IS 3
SP 101
OP 106
AB 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 < 0.001 ) and 99.31% ( p < 0.001 ), 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.
K1 activation functions
K1 silent listening condition
K1 electroencephalogram
K1 background noise
K1 human brain
K1 multivariate matrices
K1 multiscale analysis
K1 singular value decomposition
K1 extreme learning machine classifier
K1 multivariate multiscale matrices
K1 music
K1 sub-band signals
K1 ANC
K1 discrete wavelet transform
K1 active noise control system
K1 singular value features
K1 multichannel EEG analysis
DO https://doi.org/10.1049/htl.2017.0016
UL https://digital-library.theiet.org/;jsessionid=5dcdovrc1k64l.x-iet-live-01content/journals/10.1049/htl.2017.0016
LA English
SN
YR 2018
OL EN