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

• Published in: International Conference on Computer, Electrical & Communication Engineering (Online) pp. 1 - 7
• Main Authors: Acharjee, Raktim, Ahamed, Shaik Rafi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.02.2024
• Subjects: 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

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...