谱自适应公共空间模式
Spectrally Adaptive Common Spatial Patterns
摘要：
公共空间模式（CSP）方法被广泛用于脑电（EEG）数据的特征提取，例如在运动想象脑机接口（BCI）系统中。它是一种数据驱动的方法，估计一组空间滤波器，使得滤波后的EEG信号的功率对于一个运动想象类最大化，而对于另一个运动想象类最小化。然而，该方法易于过拟合，并且已知具有较差的泛化能力，特别是在有限的校准数据下。此外，由于大脑数据的高度异质性和大脑活动的非平稳性，CSP通常为每个用户单独训练，导致校准时间长或频繁重新校准，这对用户来说很累。在这项工作中，我们提出了一种新的算法称为频谱自适应公共空间模式（SACSP），提高CSP通过学习时间/频谱滤波器为每个空间滤波器，使空间滤波器集中在最相关的时间频率为每个用户。我们显示SACSP的功效，提供更好的概括性和更高的分类精度，从校准到在线控制相比，现有的方法。此外，我们表明，SACSP提供神经生理相关的信息的时间频率的过滤信号。我们的研究结果突出了BCI用户之间的运动想象信号的差异以及为每个类别生成的信号的频谱差异，并显示了以数据驱动的方式学习强大的用户特定功能的重要性。
英文摘要：
The method of Common Spatial Patterns (CSP) is widely used for feature extraction of electroencephalography (EEG) data, such as in motor imagery brain-computer interface (BCI) systems. It is a data-driven method estimating a set of spatial filters so that the power of the filtered EEG signal is maximized for one motor imagery class and minimized for the other. This method, however, is prone to overfitting and is known to suffer from poor generalization especially with limited calibration data. Additionally, due to the high heterogeneity in brain data and the non-stationarity of brain activity, CSP is usually trained for each user separately resulting in long calibration sessions or frequent re-calibrations that are tiring for the user. In this work, we propose a novel algorithm called Spectrally Adaptive Common Spatial Patterns (SACSP) that improves CSP by learning a temporal/spectral filter for each spatial filter so that the spatial filters are concentrated on the most relevant temporal frequencies for each user. We show the efficacy of SACSP in providing better generalizability and higher classification accuracy from calibration to online control compared to existing methods. Furthermore, we show that SACSP provides neurophysiologically relevant information about the temporal frequencies of the filtered signals. Our results highlight the differences in the motor imagery signal among BCI users as well as spectral differences in the signals generated for each class, and show the importance of learning robust user-specific features in a data-driven manner.
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