Úbeda Andrés, Iáñez Eduardo, Azorin José M
Virtual Reality and Robotics Lab, University Miguel Hernández, Elche, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6303-6. doi: 10.1109/IEMBS.2011.6091555.
This paper describes a classifier based on image correlation of EEG maps to distinguish between three mental tasks in a Brain-Computer Interface (BCI). The data set V of BCI Competition 2003 has been used to test the classifier. To that end, the EEG maps obtained from this data set have been studied to find the ideal parameters of processing time and frequency. The classifier designed is based on a normalized cross-correlation of images which makes possible to obtain a proper similarity index to perform the classification. The success percentage of the classifier has been shown for different combinations of data. The results obtained are very successful, showing that this kind of techniques may be able to classify between three mental tasks with good results in a future online testing.
本文描述了一种基于脑电图（EEG）图谱图像相关性的分类器，用于区分脑机接口（BCI）中的三种心理任务。使用了2003年BCI竞赛比赛竞赛的数据集V来测试该分类器。为此，对从该数据集中获得的EEG图谱进行了研究，以找到处理时间和频率的理想参数。所设计的分类器基于图像的归一化互相关，这使得能够获得合适的相似性指标来进行分类。已展示了该分类器在不同数据组合下的成功率。获得的结果非常成功，表明这种技术在未来的在线测试中可能能够很好地对三种心理任务进行分类。
