In a motor imagery-based brain–computer interface system, an effective classifier is required. However, the effectiveness of classifier is substantially influenced by the individual differences among electroencephalography (EEG) signals and artifacts. Therefore, in this study, we adopted an ensemble method by combining various classifiers, including a fuzzy classifier that can reduce the influence of artifacts, to improve the robustness and accuracy in classification across participants. Nine participants were recruited for the experiment and asked to perform a left- and right-hand motor imagery task. We calculated the classification rates obtained with the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Naive Bayes, support vector machine (SVM), and fuzzy twin SVM (FTSVM) classifiers based on the spectral features extracted by an autoregressive (AR) model and the spectral–temporal features extracted by the Morlet wavelet from overlapped 1.024-s EEG segments. The fivefold cross-validation accuracies of the ensemble method for the 1.024-s EEG were 71.39% and 73.06% with the AR- and wavelet-extracted features, respectively. In the comparison of individual classifiers, the Linear-FTSVM method outperformed other individual classifiers. In addition, the ensemble model with the inclusion of FTSVM classifiers performs superior to the ensemble models without using FTSVM classifiers.

在基于运动意象的脑机接口系统中，需要一个有效的分类器。然而，分类器的有效性在很大程度上受到脑电图 (EEG) 信号和伪影之间的个体差异的影响。因此，在本研究中，我们通过组合各种分类器采用集成方法，包括可以减少伪影影响的模糊分类器，以提高参与者分类的鲁棒性和准确性。为该实验招募了 9 名参与者，并要求他们执行左手和右手运动想象任务。我们计算了通过线性判别分析 (LDA)、二次判别分析 (QDA)、朴素贝叶斯、支持向量机 (SVM)、基于自回归 (AR) 模型提取的光谱特征和 Morlet 小波从重叠的 1.024 秒 EEG 段提取的光谱-时间特征的模糊孪生 SVM (FTSVM) 分类器。1.024-s EEG 集成方法的五重交叉验证精度分别为 71.39% 和 73.06%，具有 AR 和小波提取特征。在个体分类器的比较中，Linear-FTSVM 方法优于其他个体分类器。此外，包含 FTSVM 分类器的集成模型的性能优于不使用 FTSVM 分类器的集成模型。具有 AR 和小波提取特征的 024 秒脑电图分别为 71.39% 和 73.06%。在个体分类器的比较中，Linear-FTSVM 方法优于其他个体分类器。此外，包含 FTSVM 分类器的集成模型的性能优于不使用 FTSVM 分类器的集成模型。具有 AR 和小波提取特征的 024 秒脑电图分别为 71.39% 和 73.06%。在个体分类器的比较中，Linear-FTSVM 方法优于其他个体分类器。此外，包含 FTSVM 分类器的集成模型的性能优于不使用 FTSVM 分类器的集成模型。