Li ShuRui, Jin Jing, Daly Ian, Liu Chang, Cichocki Andrzej
East China University of Science and Technology, Meilong Road, Shanghai, 200237, CHINA.
East China University of Science and Technology,
J Neural Eng. 2021 Dec 13. doi: 10.1088/1741-2552/ac42b4.
Brain-computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.
脑机接口（BCI）系统对脑电图信号进行解码，以建立一个人类大脑与外部世界直接交互的通道，而无需肌肉或神经控制。P300拼写器是应用最广泛的BCI应用之一，它向用户呈现一系列字符，并通过识别脑电图中的P300事件相关电位来进行字符识别。这种基于P300的BCI系统可以达到较高的准确率，但由于冗余和噪声信号，在日常生活中难以使用。应该考虑改进的空间。我们为基于P300的BCI系统提出了一种新颖的混合特征选择方法，以解决特征冗余问题，该方法结合了门格尔曲率和线性判别分析。首先，将选定的策略分别应用于给定的数据集，以估计应用于每个特征的增益。然后，将每个生成的值集按降序排列，并根据预定义的标准进行判断，以确定其在分类模型中的适用性。然后评估两种方法的交集，以确定最佳特征子集。使用三个公共数据集，即BCI竞赛III数据集II、BNCI地平线数据集和EPFL数据集，对所提出的方法进行了评估。实验结果表明，与其他典型的特征选择和分类方法相比，我们提出的方法具有更好或相当的性能。此外，我们提出的方法在三个数据集中的所有epoch之后都能实现最佳分类准确率。总之，我们提出的方法为提高基于P300的BCI拼写器的性能提供了一种新途径。
