Automatic Eyeblink Artifact Removal from Single Channel EEG Signals Using One-Dimensional Convolutional Denoising Autoencoder

Eyeblink artifacts are common and significant disruption that occurs when the subject blinks during the recording of the Electroencephalogram (EEG) signals. Removing this artifact is essential to ensure the precision and reliability of the recorded brain activity. Compared to multichannel EEG system...

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Veröffentlicht in:	International Conference on Computer, Electrical & Communication Engineering (Online) S. 1 - 7
Hauptverfasser:	Acharjee, Raktim, Ahamed, Shaik Rafi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 02.02.2024
Schlagworte:	Automatic Artifact Removal Brain modeling Brain-Computer Interface (BCI) Convolution Convolutional Denoising Autoencoder Correlation coefficient Decoding Electroencephalogram (EEG) Electroencephalography Eyeblink Artifact Noise reduction Single Channel EEG Training
ISSN:	2768-0576
Online-Zugang:	Volltext
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