A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images

A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images

Brain–computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studi...

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Bibliographic Details
Main Authors: Devi, N.D (Author), Majumder, S. (Author), Meitei, T.G (Author), Singh, S.A (Author)
Format: Article
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2021
Subjects:
Article
Bandpass filters
Biomedical signal processing
Brain computer interface
Brain–computer interface
continuous wavelet transform
Continuous wavelet transform
Continuous Wavelet Transform
convolutional neural network
deep neural network
Deep neural network
Deep neural networks
diagnostic accuracy
Disabled people
electroencephalogram
Electroencephalogram
Electroencephalogram signals
Electroencephalography
human
Interface modeling
Learning approach
Motor disability
Network-based
P300
Performance
Single channels
Transfer learning
transfer of learning
Wavelet transforms
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