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Tracking the Transitions of Brain States: An Analytical Approach Using EEG.
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.9 ) Pub Date : 2020-06-30 , DOI: 10.1109/tnsre.2020.3005950
Jyoti Maheshwari , Shiv Dutt Joshi , Tapan K. Gandhi

Objective: Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain. Methods: Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest in terms of changes in the invariant spaces spanned by eigen vectors as well as amount of variance along them. Based on this, our first approach is to track the brain state transitions by analysing invariant space variations over time. Whereas, our second approach analyses sub-band characteristic response vector formed using eigen values along with the eigen vectors to capture the dynamics. Result: We have taken two real time EEG datasets to demonstrate the efficacy of proposed approaches. It has been observed that in case of unimodal experiment, invariant spaces explicitly show the transitions of brain states. Whereas sub-band characteristic response vector approach gives better performance in the case of cross-modal conditions. Conclusions: Evolution of invariant spaces along with the eigen values may help in understanding and tracking the brain state transitions. Significance: The proposed approaches can track the activity transitions in real time. They do not require any training dataset.

中文翻译:

跟踪脑状态的转变:使用脑电图的分析方法。

目的:对大脑神经活动的分类是大脑计算机界面领域的一个众所周知的问题。基于机器学习的大脑活动分类方法并未揭示人脑的基本动态。方法:由于发现本征分解在各种应用中很有用,因此我们推测脑状态的变化将以本征向量跨越的不变空间的变化以及沿它们的方差量来体现。基于此,我们的第一种方法是通过分析随时间的不变空间变化来跟踪大脑状态的转变。而我们的第二种方法分析了使用特征值和特征向量形成的子带特征响应向量来捕获动态。结果:我们采用了两个实时EEG数据集来证明所提出方法的有效性。已经观察到在单峰实验的情况下,不变空间明确显示出大脑状态的转变。子带特征响应矢量方法在交叉模态条件下具有更好的性能。结论:不变空间的演化以及特征值可能有助于理解和跟踪大脑状态的转变。启示:所提出的方法可以实时追踪活动的转变。他们不需要任何训练数据集。子带特征响应矢量方法在交叉模态条件下具有更好的性能。结论:不变空间的演化以及特征值可能有助于理解和跟踪大脑状态的转变。启示:所提出的方法可以实时追踪活动的转变。他们不需要任何训练数据集。子带特征响应矢量方法在交叉模态条件下具有更好的性能。结论:不变空间的演化以及特征值可能有助于理解和跟踪大脑状态的转变。启示:所提出的方法可以实时追踪活动的转变。他们不需要任何训练数据集。
更新日期:2020-08-08
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