EEG-based real-time monitoring and intervention of human memory

基于脑电图的人类记忆实时监测与干预

• 批准号: RGPIN-2017-06866
• 负责人: Fukuda, Keisuke
• 金额: $ 3.35万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764194
• 关键词: EEG; based; real; time; monitoring

Our life hinges on our ability to encode new information in order to adapt our behavior to an ever changing environment. Although our long-term memory (LTM) allows us to encode and store a virtually infinite amount of information, our ability to encode new information into long-term memory varies not only across individuals but also from moment to moment within individuals. Is there any way to monitor this fluctuation of memory encoding ability in real time? And if so, could we utilize this information to increase the efficacy and efficiency of memory encoding? Recently, I have demonstrated that we can monitor this fluctuation in real time and make learning more efficient based on electroencephalograms (EEGs) recorded during learning. In this proposal, I will further advance this novel and impactful approach by pursuing the following three goals. First, I will examine the nature of voluntary control on memory encoding and its side effects. Second, I will generalize the EEG-based memory monitoring and intervention to various kinds of learning. Third, I will refine the EEG-based memory monitoring and intervention to maximize the benefit in learning. These studies will not only contribute to the theories of memory control and memory encoding but also lead to a wide range of possibilities for real-life applications of EEG-based memory monitoring and intervention.

我们的生活取决于我们编码新信息的能力，以便使我们的行为适应不断变化的环境。虽然我们的长期记忆（LTM）允许我们编码和存储几乎无限数量的信息，但我们将新信息编码到长期记忆中的能力不仅在个体之间存在差异，而且在个体内部也存在差异。有没有办法在真实的时间内监测这种记忆编码能力的波动？如果是这样的话，我们是否可以利用这些信息来提高记忆编码的效力和效率？最近，我证明了我们可以在真实的时间内监测这种波动，并根据学习过程中记录的脑电图（EEG）使学习更有效。在本提案中，我将通过追求以下三个目标来进一步推进这种新颖且有影响力的方法。首先，我将研究记忆编码的自愿控制的性质及其副作用。其次，我将把基于脑电的记忆监测和干预推广到各种学习中。第三，我将改进基于EEG的记忆监测和干预，以最大限度地提高学习的效益。这些研究不仅有助于记忆控制和记忆编码的理论，而且还为基于EEG的记忆监测和干预的实际应用提供了广泛的可能性。