Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

• Published in: Measurement. Sensors Vol. 25; p. 100616
• Main Authors: Pawan, Dhiman, Rohtash
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2023; Elsevier
• Subjects: Brain computer interface (BCI); Electroencephalogram (EEG); Machine learning; Motor imagery (MI); Pearson correlation coefficient (PCC); Wavelet packet decomposition (WPD); Motor imagery (MI); Wavelet packet decomposition (WPD); Electroencephalogram (EEG); Pearson correlation coefficient (PCC); Brain computer interface (BCI); Machine learning

Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data is a significant part of the Brain-Computer Interface (BCI) technology that allows motor-disabled persons to connect with external devices. Channel selection, feature extraction, and classification are essential requir...