Abstract：
BACKGROUND: Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability.
METHODS: Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation-and-memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time.
RESULTS: The induced memory effect (the difference between recall rates when presenting items during predicted good vs. poor learning periods) increased with the accuracy of neural decoding.
METHODS: This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement.
CONCLUSIONS: These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.
METHODS: Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation-and-memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time.
RESULTS: The induced memory effect (the difference between recall rates when presenting items during predicted good vs. poor learning periods) increased with the accuracy of neural decoding.
METHODS: This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement.
CONCLUSIONS: These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.
摘要：
背景：学习过程中人类脑电图（EEG）记录的频谱特征可预测随后的回忆变异性。
方法：利用这些波动的神经特征，我们开发了一种非侵入性闭环（NICL）系统，用于实时优化人类学习。参与者玩虚拟导航和记忆游戏；记录几天的多会话数据使我们能够构建特定于参与者的召回成功分类模型。在随后的闭环会话中，我们的平台操纵了内存编码的时序，根据实时解码的EEG特征，在预测的记忆功能良好或不良期间选择性地呈现项目。
结果：我们观察到更大的记忆调节(在预测的好与好的期间呈现项目时召回率之间的差异学习周期差)，用于样本外分类精度较高的参与者。
方法：这项研究表明，在自然的虚拟导航任务中，从EEG记录中进行的比机会更大的记忆解码比基本的单词列表回忆范式具有更大的现实有效性。在这里，我们通过根据非侵入性头皮脑电图记录定时刺激呈现来调节记忆，而之前的记忆改善闭环研究涉及颅内记录和直接电刺激。其他非侵入性研究已经调查了使用神经反馈或补救研究来改善记忆。
结论：这些发现为使用非侵入性闭环技术通过有原则的刺激时机来优化人类学习和记忆提供了一个概念验证，但仅在那些分类器可靠地预测样本外记忆功能的参与者中。
方法：利用这些波动的神经特征，我们开发了一种非侵入性闭环（NICL）系统，用于实时优化人类学习。参与者玩虚拟导航和记忆游戏；记录几天的多会话数据使我们能够构建特定于参与者的召回成功分类模型。在随后的闭环会话中，我们的平台操纵了内存编码的时序，根据实时解码的EEG特征，在预测的记忆功能良好或不良期间选择性地呈现项目。
结果：我们观察到更大的记忆调节(在预测的好与好的期间呈现项目时召回率之间的差异学习周期差)，用于样本外分类精度较高的参与者。
方法：这项研究表明，在自然的虚拟导航任务中，从EEG记录中进行的比机会更大的记忆解码比基本的单词列表回忆范式具有更大的现实有效性。在这里，我们通过根据非侵入性头皮脑电图记录定时刺激呈现来调节记忆，而之前的记忆改善闭环研究涉及颅内记录和直接电刺激。其他非侵入性研究已经调查了使用神经反馈或补救研究来改善记忆。
结论：这些发现为使用非侵入性闭环技术通过有原则的刺激时机来优化人类学习和记忆提供了一个概念验证，但仅在那些分类器可靠地预测样本外记忆功能的参与者中。
