Classifying mental states with machine learning algorithms using alpha activity decline
نویسندگان
چکیده
This publication aims at developing computer based learning environments adapting to learners’ individual cognitive condition. The adaptive mechanism, based on Brain-Computer-Interface (BCI) methodology, relays on electroencephalogram (EEG)-data to diagnose learners’ mental states. A first within-subjects study (10 students) was accomplished aiming at differentiating between states of learning and non-learning by means of EEG-data. SupportVector-Machines classified characteristics in the EEG-signals for these two different stimuli on average as 74.55% correct. For individual students the percentage of correct classification reached 92.22%. The results indicate that continuous EEG-data combined with BCI methodology is a promising approach to measuring learners’ mental states online.
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