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

Studying on EEG (Electroencephalography) data instances to dis?cover potential recognizable patterns has been a emerging hot topic in recentyears, particularly for cognitive analysis in online education areas. Machinelearning techniques have been widely adopted in EEG analytical processes fornon-invasive brain research. Existing work indicated that human brain canproduce EEG signals under the stimulation of specific activities. This paperutilizes an optimized data analytical model to identify statuses of brain wave andfurther discover brain activity patterns. The proposed model, i.e. Seg?mented EEG Graph using PLA (SEGPA), that incorporates optimized dataprocessing methods and EEG-based analytical for EEG data analysis. The datasegmentation techniques are incorporated in SEGPA model. This researchproposes a potentially efficient method for recognizing human brain activitiesthat can be used for machinery control. The experimental results reveal thepositive discovery in EEG data analysis based on the optimized samplingmethods. The proposed model can be used for identifying students cognitivestatuses and improve educational performance in COVID19 period.