The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p <; 0.05).

中文翻译：

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基于双谱的通道选择，用于基于运动想象的脑机接口


基于运动想象（MI）的脑机接口（BCI）的性能很容易受到多通道脑电图（EEG）中存在的噪声和冗余信息的影响。为了解决这个问题，已经提出了许多基于时间和空间特征的信道选择方法。然而，时间和空间特征并不能准确反映振荡脑电图功率的变化。因此，MI 相关 EEG 信号的频谱特征可能对通道选择有用。双谱分析是一种为从非线性和非高斯信号中提取非线性和非高斯信息而开发的技术。从双谱分析中提取的特征可以提供有关脑电图的频域信息。因此，在本研究中，我们提出了一种基于双谱的信道选择（BCS）方法，用于基于 MI 的 BCI。该方法使用从双谱分析中提取的对数幅度之和（SLA）和一阶谱矩（FOSM）特征来选择没有冗余信息的EEG通道。使用三个公共 BCI 竞赛数据集（BCI 竞赛 IV 数据集 1、BCI 竞赛 III 数据集 IVa 和 BCI 竞赛 III 数据集 IIIa）来验证我们提出的方法的有效性。结果表明，我们的 BCS 方法优于所有通道的使用（分别为 83.8% vs 69.4%、86.3% vs 82.9% 和 77.8% vs 68.2%）。此外，与其他最先进的方法相比，我们的 BCS 方法还可以为基于 MI 的 BCI 实现明显更好的分类精度（Wilcoxon 签名测试，p <；0.05）。