Deep Learning on EEG Study Concentration in Pendemic
DOI:
https://doi.org/10.30872/jim.v16i2.6474Keywords:
Brainwaves, Electroencephalogram, Deep Learning, CNN, Fast Fourier TransformAbstract
Brainwaves are one of the biometric properties that can be used to identify individuals based on their physical and behavioural characteristics. An electroencephalogram (EEG) can be used to measure and capture brain wave activity. The activities required are in the form of giving complex tasks to get thinking and concentration processes called Cognitive Tests, in the form of a Culture Fair Intelligence Test (CFIT) stimulus and Competency Test (UK). This study aims to obtain a pattern of the relationship between concentration and learning outcomes for late adolescent students during the pandemic. The object of research involved in this research is the 10th grade students of TKJ SMK. Data acquisition was carried out twice on the Beta signal by doing cognitive test questions which were done twice at school and at home. Then the data obtained from the test results will be extracted using Fast Fourier Transform (FFT). Furthermore, after the data extraction results are obtained, the classification process will be carried out using the CNN algorithm. The results of the FFT obtained the average value of the signal peak. The results of the CNN classification show that the pandemic does not affect student concentration. The average signal concentration in schools when testing using CFIT is 0.2445 and at the time of testing using UK Mathematics is 0.1330 with an average CFIT score of 77.05 and for UK average is 53.33 with an accuracy value of 83.33 %. While the average signal concentration at home when testing using CFIT is 0.2252 and at the time of testing using UK Mathematics is 0.1301 with an average CFIT score of 77.13 and for UK average is 57.50 with an accuracy value of 83, 33%.
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