Background and Objective

Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined.

Methods

In order to determine the subject-specific optimal feature subsets, stepwise regression analysis based on sequential feature selection (SWR-SFS) and ReliefF methods were employed. Feature selection is applied on time-domain features of fNIRS signals such as mean, slope, peak, skewness and kurtosis values of signals. Linear discriminant analysis, k nearest neighborhood and support vector machines are employed to evaluate the performance of the selected feature subsets. The proposed techniques are validated on benchmark motor imagery (MI) and mental arithmetic (MA) based fNIRS datasets collected from 29 healthy subjects.

Results

Both SWR-SFS and reliefF feature selection methods have significantly improved the classification accuracy. However, the best results (88.67% (HbR) and 86.43% (HbO) for MA dataset and 77.01% (HbR) and 71.32% (HbO) for MI dataset) were achieved using SWR-SFS while feature selection provided extremely high feature reduction rates (89.50% (HbR) and 93.99% (HbO) for MA dataset and 94.04% (HbR) and 97.73% (HbO) for MI dataset).

Conclusions

The results of the study indicate that employing feature selection improves both MA and MI-based fNIRS signals classification performance significantly.

中文翻译：

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基于近红外光谱的脑机接口的主题特定功能选择。

背景与目的

脑机接口（BCI）使人们能够通过分析大脑的神经活动来控制外部设备。功能性近红外光谱（fNIRS）是一种新兴的光学成像技术，经常用于无创BCI中。确定主题特定功能是提高分类准确性以及降低基于fNIRS的BCI系统的复杂性的重要考虑因素。在这项研究中，研究了针对特定对象的特征选择对fNIRS信号分类准确性的有效性。

方法

为了确定特定于受试者的最佳特征子集，采用了基于顺序特征选择（SWR-SFS）和ReliefF方法的逐步回归分析。特征选择应用于fNIRS信号的时域特征，例如信号的均值，斜率，峰值，偏度和峰度值。线性判别分析，k最近邻和支持向量机用于评估所选特征子集的性能。基于从29名健康受试者收集的fNIRS数据集的基准运动图像（MI）和心理算术（MA）对所提出的技术进行了验证。

结果

SWR-SFS和reliefF特征选择方法都大大提高了分类精度。但是，使用SWR-SFS可获得最佳结果（MA数据集为88.67％（HbR）和86.43％（HbO），MI数据集为77.01％（HbR）和71.32％（HbO）），而特征选择提供了极高的特征约简率（MA数据集为89.50％（HbR）和93.99％（HbO），MI数据集为94.04％（HbR）和97.73％（HbO））。

结论

研究结果表明，采用特征选择可以显着改善基于MA和MI的fNIRS信号分类性能。