SBIR Phase I: Brain Wave Adaptive Learning for Accelerated Adaptive Learning for STEM Education

SBIR第一阶段项目将开发一个脑机接口（BCI），用于STEM教育的加速自适应学习。 首次将基于脑电头带的脑机接口应用于基于学习策略的自适应学习。 脑电图是一种非侵入性的测量脑电波模式的方法，用于识别大脑中的电活动。拟议的学习系统将提供五种学习策略-学徒，附带，归纳，演绎和发现与实时学习者分析。 基于脑电头带的自适应学习脑机接口将为学习者提供真实的实时神经反馈，以确定最佳学习策略。 预期使用多通道EEG头带直接收集脑电波数据将导致更快地收敛到最佳学习策略，以使学习者达到最大的学习效果。这种方法将允许我们将联合收割机脑波分析与实时统计推断相结合，以提高学习者的性能。 脑电波数据和学习者表现的多元相关分析的组合将使所提出的BCI方法的加速自适应学习的验证。随着EEG头带技术和低能量蓝牙接口的发展，将有可能将所提出的方法商业化，用于学校和大学的STEM教育。使用EEG头带加速自适应学习的脑机接口将打破许多新的技术基础。首次将学习者个人的脑电波数据用于识别个人学习偏好和学习策略。阿尔法，贝塔和伽玛脑电波的分布将提供丰富的信息的大脑状态的学习者暴露于差异化的学习策略。脑机接口的方法将导致加速识别的学习策略的最佳学习效果的基础上，对个人学习者的真实的-时间脑电波分析。拟议项目还将提供前所未有的机会，以评估神经反馈对STEM学生学习成果的影响。 该项目将引发脑电波自适应学习的新技术趋势，为高堆栈学习和培训产生许多新的应用和产品。基于脑机接口的自适应学习还将帮助患有认知障碍（自闭症谱系），ADD和ADHD的学生作为教育计划的学习辅助。具有数据驱动反馈的脑电波自适应学习对于提高STEM教育的完成率和毕业率非常有用。

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.