Objective

For decades, a great deal of interest in investigating brain network functional connective features has arisen in brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). Traditional decoding algorithms, for example, canonical correlation analysis (CCA), only consider the inherent properties of each channel in terms of feature extraction for the single channel electroencephalogram (EEG) signal, with inadequate features that cannot fully utilize the information transmitted by the brain.

Material and methods

This paper proposes a fused feature extraction method, CCA-DTF, which combines CCA with a direct transfer function (DTF) to construct local brain network features with seven leads in the occipital region. First, the features extracted by the CCA algorithm were combined with these features extracted by DTF to analyze the EEG data from 20 subjects. Then, two methods, support vector machine (SVM) and random forest (RF), were used in constructing the classifiers for the four tasks classification of the SSVEP-BCI.

Results

The experimental results showed that incorporating local network features (extracted from DTF or Pearson correlation coefficient) can effectively improve the classification average accuracy and the information transfer rate (ITR) of SSVEP, not only for SVM but also for the ensemble method RF. In particular, CCA-DTF plus SVM obtained a 94.52% classification average accuracy and a 49.23 bits/min ITR in a time window of 2 seconds. The performance was 5.57% and 8.01 bits/min higher than those of traditional CCA plus SVM, respectively.

Conclusion

The proposed feature extraction method based on local network features is robust for improving the performance of SSVEP-BCI significantly, which has a perspective of being used in neural rehabilitation engineering field.

中文翻译：

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局部网络特性对SSVEP脑机接口性能的影响

客观的

几十年来，基于稳态视觉诱发电位 (SSVEP) 的脑机接口 (BCI) 引起了人们对研究脑网络功能连接特征的极大兴趣。传统的解码算法，例如典型相关分析（CCA），在对单通道脑电图（EEG）信号进行特征提取时仅考虑每个通道的固有特性，特征不足，无法充分利用大脑传递的信息.

材料与方法

本文提出了一种融合特征提取方法CCA-DTF，该方法将CCA与直接传递函数（DTF）相结合，构建枕区七导联的局部脑网络特征。首先，将CCA算法提取的特征与DTF提取的这些特征相结合，对20名受试者的脑电数据进行分析。然后，两种方法，支持向量机（SVM）和随机森林（RF）被用于构建分类器用于SSVEP-BCI的四个任务分类。

结果

实验结果表明，结合局部网络特征（从 DTF 或 Pearson 相关系数中提取）可以有效提高 SSVEP 的分类平均准确率和信息传输率（ITR），不仅适用于 SVM，也适用于集成方法 RF。特别是，CCA-DTF 加 SVM 在 2 秒的时间窗口内获得了 94.52% 的分类平均准确度和 49.23 位/分钟的 ITR。性能分别比传统 CCA 加 SVM 高 5.57% 和 8.01 位/分钟。

结论

所提出的基于局部网络特征的特征提取方法对于显着提高SSVEP-BCI的性能具有鲁棒性，具有应用于神经康复工程领域的前景。