Chaudhary Mahima, Adams Meaghan S, Mukhopadhyay Sumona, Litoiu Marin, Sergio Lauren E
Lassonde School of Engineering, York University, Toronto, ON, Canada.
Faculty of Health, York University, Toronto, ON, Canada.
Front Hum Neurosci. 2021 Oct 8;15:662875. doi: 10.3389/fnhum.2021.662875. eCollection 2021.
Objective clinical tools, including cognitive-motor integration (CMI) tasks, have the potential to improve concussion rehabilitation by helping to determine whether or not a concussion has occurred. In order to be useful, however, an individual must put forth their best effort. In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels. By treating the 2D scalogram as an image, we show that the trained CNN-LSTM classifier based on automated visual analysis can achieve high levels of discrimination and an overall accuracy of 98.71% in case of intra-subject classification, as well as low false-positive rates. The average intra-subject accuracy obtained was 92.8%, and the average inter-subject accuracy was 86.15%. These results indicate that our proposed model performed well on the data of all subjects. We also compare the scalogram-based results with the results that we obtained by using raw time-series, showing that scalogram-based gave better performance. Our method can be applied in clinical applications such as baseline testing, assessing the current state of injury and recovery tracking and industrial applications like monitoring performance deterioration in workplaces.
客观临床工具，包括认知运动整合（CMI）任务，有潜力通过帮助确定是否发生脑震荡来改善脑震荡康复。然而，为了发挥作用，个体必须尽最大努力。在本研究中，我们提出了一种新颖的方法，使用深度学习（DL）方法对脑电图（EEG）信号进行分析，以检测最佳努力（无破坏）和故意表现不佳（破坏）之间的皮质活动差异。在CMI任务期间，从可穿戴的四通道头带获取EEG信号。每个参与者以随机顺序完成破坏和无破坏条件。使用具有自注意力的多通道卷积神经网络与长短期记忆（CNN-LSTM）模型，通过将时间序列转换为基于二维（2D）图像的小波图表示，将时间序列分类为破坏和无破坏。这种方法允许检查EEG的基于频率和时间的特征，并且使用多通道模型有助于捕获不同EEG通道之间的相关性和因果关系。通过将2D小波图视为图像，我们表明基于自动视觉分析训练的CNN-LSTM分类器在个体内分类的情况下可以实现高水平的辨别力和98.71%的总体准确率，以及低假阳性率。获得的个体内平均准确率为92.8%，个体间平均准确率为86.15%。这些结果表明我们提出的模型在所有受试者的数据上表现良好。我们还将基于小波图的结果与使用原始时间序列获得的结果进行比较，表明基于小波图的结果表现更好。我们的方法可应用于临床应用，如基线测试、评估当前损伤状态和恢复跟踪，以及工业应用，如监测工作场所的性能恶化。
