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

SBIR 第一阶段：脑波自适应学习加速 STEM 教育自适应学习

• 批准号: 1549256
• 负责人: Nishikant Sonwalkar
• 金额: $ 15万
• 依托单位: EDWISETECH INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549256&HistoricalAwards=false
• 关键词: SBIR; Phase; Brain; Wave; Adaptive

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.

该 SBIR 第一阶段项目将开发脑机接口 (BCI)，用于 STEM 教育的加速自适应学习。 使用脑电图（EEG）头带的脑机接口将首次应用于基于自适应学习的学习策略。 脑电图是一种测量脑电波模式以识别大脑电活动的非侵入性方法。拟议的学习系统将提供五种学习策略——学徒式、附带式、归纳式、演绎式和带有实时学习者分析的发现式。 基于脑电图头带的自适应学习脑机接口将为学习者提供实时神经反馈，以识别最佳学习策略。 预计使用多通道脑电图头带直接收集脑电波数据将更快地收敛到最佳学习策略，使学习者达到最大的学习成果。这种方法将使我们能够将脑电波分析与实时统计推断结合起来，以提高学习者的表现。 脑电波数据和学习者表现的多变量相关分析相结合，将能够验证所提出的用于加速自适应学习的 BCI 方法。随着 EEG 头带技术和低能量蓝牙接口的发展，学校和学院的 STEM 教育所提出的方法将有可能商业化。使用 EEG 头带加速自适应学习的脑机接口将突破许多新技术领域。首次利用个体学习者的脑电波数据来识别个人学习偏好和学习策略。 Alfa、Beta 和 Gamma 脑电波的分布将为接受差异化学习策略的学习者的大脑状态提供丰富的信息。 BCI 方法将基于个体学习者的实时脑电波分析，加速识别学习策略，以获得最佳学习成果。拟议的项目还将提供前所未有的机会来评估神经反馈对 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，从而为高堆栈学习和培训产生大量新的应用程序和产品。基于脑机接口的适应性学习还将帮助患有认知障碍（自闭症谱系）、ADD 和 ADHD 的学生作为教育项目的学习辅助工具。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。