NEUROSCIENCE PERSPECTIVES EEG ANALYSIS OF EYE BLINKING ACTIVITY (KEY IJP************437)

• Dimpi Dewangan

Abstract

The purpose of this study is to investigate how eye blink artifacts interact with electroencephalogram (EEG) signals and develop reliable detection and removal techniques. Eye blinking introduces significant noise into the EEG recordings, which can interfere with neurophysiological analysis.(Ahammed & Ahmed, 2020) The ability to identify and reduce these artifacts is essential for enhancing the reliability of the EEG-based studies. Eye blink artifact detection plays an important role in EG analysis and neurodegenerative disorders, particularly with the frontal epilepsy discharges.(Wang et al., 2021) EEG signals are sometimes tainted by artifacts and have a frequency range of 0-100 Hz. Technical artifacts such as electric power source noise and amplitude artifacts, as well as biological artifacts including ocular, ECG, and EMG artifacts, are all present in the EEG. One of the primary artifacts in the EEG data is the blink of the eye. The identification of eye blinks utilizing kurtosis and amplitude analysis of EEG signals is the main topic of this research.(Chambayil et al., 2010) A technology called a brain computer interface (BCI) enables communication between people without the use of their hands or mouths. The subject's intention is encoded into his electroencephalogram (EEG), which is a scalp recording obtained from his brain. Artefacts are noises that are added to the EEG signal by sources of electric fields both inside and outside the human body that do not involve the central nervous system (CNS). The artifacts should be appropriately managed as they obstruct the signal analysis. Electrooculographic (EOG) artefacts are the most prevalent and distinctive type, particularly those that are eye-blinking.(Manoilov, 2007) To improve classification accuracy and further the development of the brain-computer interface (BCI), it is imperative to remove these artifacts from EEG data as efficiently as possible. In this paper, we used a hybrid EEG and eye tracker system to identify and remove ocular artifacts from EEG data by proposing an automatic methodology based on independent component analysis (ICA) and system identification. The suggested algorithm's performance is demonstrated with both conventional and experimental EEG datasets. The suggested technique maintains the neural activity associated EEG signals in the non-artifactual zone while simultaneously eliminating the ocular artifacts from the artifactual zone. The suggested algorithm performs noticeably better than the two cutting-edge methods, ADJUST based ICA and REGICA, when it comes to eliminating eye movement and blink artifacts from EEG data.(Mannan et al., 2016)