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结合神经和行为测量可增强适应性训练。

Combining Neural and Behavioral Measures Enhances Adaptive Training.

作者信息

Rahman Md Lutfor, Files Benjamin T, Oiknine Ashley H, Pollard Kimberly A, Khooshabeh Peter, Song Chengyu, Passaro Antony D

机构信息

Department of Computer Science and Information Systems, California State University San Marcos, San Marcos, CA, United States.

Human Research and Engineering Directorate, DEVCOM Army Research Laboratory, Los Angeles, CA, United States.

出版信息

Front Hum Neurosci. 2022 Feb 14;16:787576. doi: 10.3389/fnhum.2022.787576. eCollection 2022.

DOI:10.3389/fnhum.2022.787576
PMID:35237140
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8882624/
Abstract

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4-13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance.

摘要

自适应训练会调整训练任务,目的是提高学习成果。研究表明,自适应训练能提升人类在注意力、工作记忆容量和运动控制任务方面的表现。此外,人们还观察到神经脑电图频谱特征(4 - 13赫兹)与某些认知任务表现之间存在相关性。这种关系表明,一些脑电图特征可能在自适应训练方案中有用。在此,我们预计在基于行为的自适应训练系统中加入神经测量指标,会提高人类在后续迁移任务中的表现。我们设计、开发并开展了一项针对44名参与者的受试者间研究,比较了三种训练方案:单项固定难度(SIFD)、基于行为的自适应训练(BAT)以及使用行为和脑电图测量指标的联合自适应训练(CAT)。结果显示,基于CAT的系统在迁移任务表现上相对于SIFD和BAT系统分别具有6个和9个百分点的统计学显著优势。我们的研究为基于用户行为表现和神经信号设计闭环脑机接口系统以增强人类表现展示了一条有前景的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/8e9efd32b1dc/fnhum-16-787576-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/fbc1446a4713/fnhum-16-787576-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/03ff7c85ae3b/fnhum-16-787576-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/9633b0901525/fnhum-16-787576-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/6f10003ee121/fnhum-16-787576-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/8e9efd32b1dc/fnhum-16-787576-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/fbc1446a4713/fnhum-16-787576-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/03ff7c85ae3b/fnhum-16-787576-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/9633b0901525/fnhum-16-787576-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/6f10003ee121/fnhum-16-787576-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/8882624/8e9efd32b1dc/fnhum-16-787576-g0005.jpg

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