A Real-Time Detection of Eye Blinks from the Electroencephalogram
Uddipan Hazarika, Bidyut Bikash Borah, Satyabrat Malla Bujar Baruah, Soumik Roy · 2024
The electroencephalogram (EEG) is frequently used as the model of a non-invasive interface for gathering information about a person&s;s mental state. There will always be artifacts in the recorded electrical activity of the brain that will confound any attempts to analyze the EEG data. Therefore, it is essential to devise strategies for identifying the artifacts and extracting the clean EEG data during encephalogram recordings. Based on the Morphological Component Analysis mechanism, the suggested system offers a real-time approach of detecting eye-blink artifacts from EEG signals acquired using a TGAM EEG sensor. The proposed method was assessed using cutting-edge datasets, revealing that the correlation or the correspondence between the unprocessed EEG and the purified signal varied within a range from 0.75 to 0.94, depending on the extent of contamination caused by both mild and severe eye blinks.