IEEE Trans Neural Syst Rehabil Eng. 2012 Jan;20(1):2-7. doi: 10.1109/TNSRE.2011.2169991. Epub 2011 Oct 13.
This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.
本文提出了一种基于神经网络和模糊理论的算法（S-dFasArt），用于对脑电图（EEG）信号中的自发性心理活动进行分类，以便操作非侵入性脑机接口。重点是解决三类问题，左手运动想象、右手运动想象和词语生成。该算法允许对时间模式进行监督分类，从而提高 BCI 竞赛 III（数据集 V：多类问题，连续 EEG）的分类率。使用竞赛提供的预计算数据并遵循其中规定的规则，提出了一种基于 S-dFasArt 的新方法，以及规则修剪和投票策略。结果与其他已发表的方法进行了比较，提高了它们的成功率。
