Gursel Ozmen Nurhan, Gumusel Levent, Yang Yuan
Department of Mechanical Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey.
Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Comput Math Methods Med. 2018 Jan 23;2018:9890132. doi: 10.1155/2018/9890132. eCollection 2018.
Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter "A." Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%), indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.
脑电图（EEG）信号分类在脑机接口（BCI）的心理解码中至关重要。我们引入了一种基于频域分析的特征提取方法，以提高使用单通道EEG对不同心理任务的分类性能。这种受生物启发的方法从EEG信号的功率谱密度（PSD）中提取最具判别力的频谱特征。我们将我们的方法应用于一个数据集，该数据集包含六名执行五种不同想象任务的受试者：（i）静息状态，（ii）心算，（iii）左手运动想象，（iv）右手运动想象，以及（v）字母“A”的想象。使用线性判别分析和支持向量机在单个EEG通道中进行成对和多类分类。我们的方法产生的结果（二元分类的平均分类准确率为83.06%，多类分类的平均分类准确率为91.85%）与最先进的方法相当，使用单通道EEG且计算成本低。在所有任务对中，心算与字母想象产生了最佳结果（平均分类准确率为90.29%），表明该任务对可能是二元类BCI最合适的任务对。这项研究有助于单通道BCI的发展，以及为用户定义的应用找到最佳任务对。
