Oikonomou Vangelis P, Nikolopoulos Spiros, Petrantonakis Panagiotis, Kompatsiaris Ioannis
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:207-210. doi: 10.1109/EMBC.2018.8512195.
Brain-computer interfaces (BCIs) make humancomputer interaction more natural, especially for people with neuro-muscular disabilities. Among various data acquisition modalities the electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work, a method based on sparse kernel machines is proposed for the classification of motor imagery (MI) EEG data. More specifically, a new sparse prior is proposed for the selection of the most important information and the estimation of model parameters is performed using the bayesian framework. The experimental results obtained on a benchmarking EEG dataset for MI, have shown that the proposed method compares favorably with state of the art approaches in BCI literature.
脑机接口（BCI）使人机交互更加自然，尤其对于患有神经肌肉残疾的人来说。在各种数据采集方式中，脑电图（EEG）因其非侵入性而占据最为突出的地位。在这项工作中，提出了一种基于稀疏核机器的方法用于运动想象（MI）脑电数据的分类。更具体地说，提出了一种新的稀疏先验用于选择最重要的信息，并使用贝叶斯框架进行模型参数估计。在一个用于运动想象的基准脑电数据集上获得的实验结果表明，所提出的方法与BCI文献中的现有方法相比具有优势。
