Zhaoheng Ni
ABSTRACT
Brain fog, also known as confusion, is one of the main reasons of the low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in human's mind in real time is a challenging and important task which can be applied to online education, driver fatigue detection and so on. In this paper, we applied Bidirectional LSTM Recurrent Neural Networks to classify students' confusions. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activities.
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Index Terms
- Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks
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