Implementation Artificial Neural Network on Microcontroller for Student Attention Level Monitoring Device Using EEG

The ability to concentrate is needed by students in the learning process. Also, the level of concentration required by each employee when they want to do various kinds of work. Monitoring and evaluating a person's concentration conditions is not easy, one of which is obtained from the information signal Electroencephalography (EEG). This study aims to detect the condition of whether someone is concentrating when doing activities or not. The method used in this study is for Backpropagation Type Neural Networks. Inputs from Artificial Neural Networks are EEG waves with Theta, Low Beta, and High Beta types. The identification system outputs are two classes, namely the full concentration class and the low concentration class. The ANN model obtained through training is then implemented into the Arduino Microcontroller so that it can be used as portable device. So, the classification results obtained are suitable with the output.