Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics.
Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics.
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DOI：
10.3389/fnins.2014.00342
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发表时间：
2014
影响因子：
4.3
通讯作者：
Wurzer D
中科院分区：
文献类型：
--
作者：
Stikic M;Berka C;Levendowski DJ;Rubio RF;Tan V;Korszen S;Barba D;Wurzer D
本研究的目的是调查生理指标的可行性，如ECG衍生的心率和EEG衍生的认知工作量和参与作为不同训练任务的性能的潜在预测因子。一种基于自组织神经网络（NN）的无监督方法被用来模拟认知状态随时间的变化。特征向量包括EEG参与度、EEG工作负荷和心率指标，所有这些指标都进行了自我归一化以考虑个体差异。在竞争训练过程中，开发了一种线性拓扑结构，其中彼此相似的特征向量激活了相同的NN节点。NN模型是根据来自51名参与者的战斗射击训练数据进行训练和自动验证的，这些参与者需要在具有挑战性的战斗场景中做出“致命的武力决定”。使用10倍交叉验证对训练的NN模型进行交叉验证。它也验证了高尔夫研究，其中额外的22名参与者被要求完成10个会议的10推杆。两项研究的激活节点的时间序列遵循相同的变化模式，证明了该方法的泛化能力。大多数节点转换变化是局部的，但重要事件通常会导致生理指标的显著变化，如较大的状态变化所证明的。这是通过计算转换分数作为激活的NN节点之间的后续状态转换的总和来研究的。相关分析表明，在两项研究中，过渡分数和受试者的表现之间存在统计学显著相关。本文探讨了生理变化的时间序列包括性能预测的判别模式的假设。这些生理标记可以用于未来的训练改进系统（例如，通过神经反馈），并应用于各种训练环境。
The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make “deadly force decisions” in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments.
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影响因子：
4.6
作者：
Ferrez, Pierre W.;Millan, Jose del R.
通讯作者：
Millan, Jose del R.
影响因子：
3.3
作者：
Gevins, A;Smith, ME;Rush, G
通讯作者：
Rush, G
影响因子：
8.2
作者：
GEHRING, WJ;GOSS, B;DONCHIN, E
通讯作者：
DONCHIN, E
影响因子：
2.6
作者：
Johnson, Robin R.;Popovic, Djordje P.;Olmstead, Richard E.;Stikic, Maja;Levendowski, Daniel J.;Berka, Chris
通讯作者：
Berka, Chris
DOI：
10.1249/00005768-199301000-00016
发表时间：
1993-01-01
期刊：
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE
影响因子：
--
作者：
CREWS, DJ;LANDERS, DM
通讯作者：
LANDERS, DM