An Optimized Self-adjusting Model for EEG Data Analysis in Online Education Processes

Studying on EEG (Electroencephalography) data instances to discover potential recognizable patterns has been a emerging hot topic in recent years, particularly for cognitive analysis in online education areas. Machine learning techniques have been widely adopted in EEG analytical processes for non-invasive brain research. Existing work indicated that human brain can produce EEG signals under the stimulation of specific activities. This paper utilizes an optimized data analytical model to identify statuses of brain wave and further discover brain activity patterns. The proposed model, i.e. Segmented EEG Graph using PLA (SEGPA), that incorporates optimized data processing methods and EEG-based analytical for EEG data analysis. The data segmentation techniques are incorporated in SEGPA model. This research proposes a potentially efficient method for recognizing human brain activities that can be used for machinery control. The experimental results reveal the positive discovery in EEG data analysis based on the optimized sampling methods. The proposed model can be used for identifying students cognitive statuses and improve educational performance in COVID19 period.