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Time-Invariant EEG Classification Based on the Fractal Dimension

机译：基于分形维数的时不变脑电分类

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摘要

Several computational techniques have been proposed in the last years to classify brain signals in order to increase the performance of Brain-Computer Interfaces. However, there are several issues that should be attended to be more friendly with the users during the calibration stage and to achieve more reliable BCI applications. One of these issues is related to the BCI's time-invariant robustness where the goal is to keep the performance using the information recorded in previous sessions to classify the data recorded in future sessions, avoiding recalibration. In order to do that, we have to carefully select the feature extraction techniques and classification algorithms. In this paper, we propose to compute the feature vector in terms of the fractal dimension. To evaluate the feasibility of the proposal, we compare the performance achieved with the fractal dimension against the coefficients of an autoregressive model using a linear discriminant classifier. To asses the time-invariant robustness of the fractal dimension, we train and evaluate the classifier using the data recorded during one day; after that, the trained classifier is evaluated using the data recorded in a different day. These experiments were done using the data set I from Brain-Computer Interface Competition III. The results show that the performance achieved with fractal dimension is better than the autoregressive model (which is one of the most common method used in BCI applications).
机译：近年来，已经提出了几种计算技术来对脑信号进行分类，以提高脑计算机接口的性能。但是，在校准阶段应注意几个问题，以便与用户更加友好并实现更可靠的BCI应用。这些问题之一与BCI的时不变健壮性有关，其目标是使用先前会话中记录的信息来保持性能，以对以后会话中记录的数据进行分类，从而避免重新校准。为此，我们必须仔细选择特征提取技术和分类算法。在本文中，我们建议根据分形维数来计算特征向量。为了评估该建议的可行性，我们使用线性判别分类器将分形维数的性能与自回归模型的系数进行了比较。为了评估分形维数的时不变鲁棒性，我们使用一天中记录的数据来训练和评估分类器。之后，将使用在不同日期记录的数据对经过训练的分类器进行评估。这些实验是使用Brain-Computer Interface Competition III中的数据集I完成的。结果表明，使用分形维数实现的性能要好于自回归模型（这是BCI应用中最常用的方法之一）。

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