Detection of Sustained Auditory Attention in Students with Visual Impairment

The efficiency of a learning process directly depends on how well the students are attentive. Detecting the level of attention can help to improve the learning quality. In recent years, there have been several attempts to leverage EEG signal processing as a tool to detect whether a student is attentive or not. In such work, the level of attention is determined by analyzing the EEG power spectrum, which is mostly followed by machine learning approaches. However, the efficiency of such methods for detecting auditory attention of blind or visually-impaired students has not been analyzed. This study aims to investigate such a scenario. To this end, we propose an EEG recording protocol to simulate the learning process of visually-impaired students as closely as possible. Ten different university students participated in the experiment. In the proposed protocol, the EEG signals were recorded by using EMOTIV EPOC+ wireless EEG headset. Then, the power spectrum of the recorded EEG signals was analyzed, and the most relevant features were extracted using the Fisher feature selection method. Then, Linear SVM, RBF SVM, KNN, and LDA classifiers were used to evaluate the proposed protocol. The results of the classification showed that the level of auditory attention could be detected up to 89% accuracy.