Obtaining immediate reliable feedback while taking class can help professors plan coursework better. Single channel EEG headsets can be used to detect a student’s mental state. In a pilot study we analyzed data collected from Kaggle using classifiers trained to detect EEG signals to identify when a student is unable to pay attention fully and is feeling confused. The classifiers were trained and tested using around 13000 videos of adults attending lectures. We found weak but significant accuracy in the EEG detecting when a student is confused and when they are not. The classifier exhibited comparable performance to a human observer in understanding body language. This pilot study promises great benefits to instructors and learners in using EEG to bridge the gap in communication of relevant emotions in the classroom.

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