Ma Teng, Li Fali, Li Peiyang, Yao Dezhong, Zhang Yangsong, Xu Peng
Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou 451191, China.
Comput Math Methods Med. 2018 Feb 26;2018:9476432. doi: 10.1155/2018/9476432. eCollection 2018.
Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy -mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.
脑电图信号和受试者的状态是非平稳的。为了有效地跟踪变化的状态，提出了一种适用于脑机接口（BCI）的自适应校准框架，该框架以运动起始视觉诱发电位（mVEP）作为控制信号。该框架的核心是为分类器训练自适应地更新训练集。更新过程包括两个操作，即向训练集中添加新样本和从训练集中移除旧样本。在所提出的框架中，支持向量机（SVM）和模糊C均值聚类（fCM）相结合，从接近当前待分类块的块中为训练集选择可靠样本。由于SVM和fCM提供的互补信息，它们可以保证输入到分类器训练中的信息的可靠性。移除过程旨在移除那些在当前新块之前记录了相对较长时间的旧样本。这两个操作可以产生一个新的训练集，该训练集可用于校准分类器以跟踪受试者不断变化的状态。实验结果表明，自适应校准框架是有效且高效的，并且可以提高在线BCI系统的性能。
