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首页> 外文期刊>Journal of Neuroscience Methods >Robust modeling based on optimized EEG bands for functional brain state inference
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Robust modeling based on optimized EEG bands for functional brain state inference

机译:基于优化的EEG谱带的健壮模型用于功能性脑状态推断

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摘要

The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30. s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.
机译:在数据驱动的方法中推断大脑状态的需求对于BCI应用以及神经科学研究至关重要。在这项工作中,我们提出了一种基于正则化线性回归分类器的新颖分类框架,该分类器由EEG(脑电图)信号的时频分解构造而成。然后使用回归来得出识别大脑状态的频率分布模型。介绍并讨论了通过交叉验证进行分类器构造,预处理和最佳正则化参数选择的过程。在从10名健康受试者记录的脑电数据上演示了该框架和特征选择技术,同时要求每30秒打开和闭合他们的眼睛。这种范例在产生从低功率(在睁眼期间)到高功率(在闭眼期间)不同的Alpha功率调制时是众所周知的。分类器经过训练可以推断出睁眼或闭眼状态,并获得了高于90%的分类精度。此外,我们的发现揭示了实验条件,EEG频率,正则化参数和分类器选择之间关系的有趣模式。这种可行的工具能够识别出任何给定脑部状态中最重要的频段,并推断出该状态时的最佳组合。与标准的傅立叶变换功率分析相比,这些功能可以提供更多的细节,这使其成为BCI建议和神经成像研究的必不可少的方法。

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