Detection of Human Attention Using EEG Signals

Attention as a cognitive aspect of brain activity is one of the most popular areas of brain studies. It has significant impact on the quality of other activities such as learning process and critical activities (e.g. driving vehicles). Because of its crucial influence on the learning process, it is one of the main aspects of research in education. In this study, we propose a brand new protocol of brain signal recording in order to classify human attention in educational environments. Unlike other protocols used to record EEG signals, our protocol does not require strong memory and strong language knowledge to carry out. To this end, we have recorded EEG signals of 12 subjects using the proposed protocol in order to achieve a valid data set for classifying human attention in two classes: attention and non-attention states. The signals have been recorded using 8 forehead channels and different features were extracted from different frequency bands. The results show that the effective features were related to the beta band and the energy of the signals. The signals were classified using KNN (K=9), c-SVM, LDA, and Bayesian classifiers and the results were compared for each of the subjects individually. On average, c-SVM and LDA classifiers were more accurate than the other methods, with 92.8% and 92.4% accuracy, respectively.