This SBIR Phase I project will develop a Brain Computer Interface (BCI) for Accelerated Adaptive Learning for STEM education. For the first time, the brain computer interface using Electroencephalogram (EEG) headband will be applied to the learning strategies based adaptive learning. Electroencephalogram is a none-invasive method for measuring brain wave pattern for identification of electrical activities in the brain. The proposed learning system will provide five learning strategies - apprentice, incidental, inductive, deductive and discovery with real-time learner analytics. The EEG headband based BCI for adaptive learning will provide real time neuro feedback to learners for identification of optimum learning strategy. It is expected that the direct collection of brain wave data using multi-channel EEG headband will lead to faster convergence to optimum learning strategy for learners to reach the maximum learning outcome. This approach will allow us to combine brain wave analytics with the real-time statistical inference to improve performance of the learner. The combination of brain wave data and multivariate correlation analysis of the learner performance will enable validation of the proposed BCI approach for accelerated adaptive learning. With the growth of EEG headband technology and low energy blue tooth interface it will be possible to commercialize proposed approach for STEM education in schools and colleges.
Brain Computer Interface for accelerate adaptive learning using EEG headbands will break numerous new technical grounds. For the first time the brain wave data of the individual learner will be used to identify personal learning preferences and learning strategy. The distribution of Alfa, Beta and Gamma brain waves will provide wealth of information on the brain state of the learner exposed to the differentiated learning strategies. The BCI approach will lead to accelerated identification of learning strategy for the best learning outcome based on the real- time brain wave analysis for individual learners. The proposed project will also provide unprecedented opportunity to assess effect of neuro feedback on the learning outcome of the STEM students. This project will spark a new technology trend of brain wave adaptive learning that will yield numerous new applications and products for high-stack learning and training. The brain computer interface based adaptive learning will also help students with cognitive disability (autism spectrum), ADD and ADHD as study aid for educational programs. The brain wave adaptive learning with data-driven feedback will be extremely useful for improving completion and graduation rates for STEM education.
