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EEG-based mind driven type writer by fuzzy radial basis function neural classifier

机译：基于模糊径向基函数神经分类器的基于脑电的思维驱动型书写器

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EEG based vowel classification is currently gaining importance for its increasing applications in the next generation mind-driven type-writing. This paper addresses a novel approach to classify the mentally uttered alphabets in a specific three lettered format, where the first and the last letter represent two vowel sounds and the middle is a space, where no character is imagined. Such formatting helps recognizing 26 alphabets in English language using seven vowel sounds only. To eliminate the possible infiltration of noise by parallel thoughts we used a specialized neuro-fuzzy classifier, where the first layer of the classifiers realized with fuzzy logic eliminates the possible creeping of noise due to side active channel interference. Two models of fuzzy preprocessing are used. The first one is realized with type-1 fuzzy logic, whereas the second model is realized with interval type-2 fuzzy sets. The latter model can take care of both intra- and inter-personal level uncertainty in measurements. Experiments undertaken reveal that the proposed type-2 fuzzy classifier outperforms both type-1 and traditional neural classifiers by a significant margin.
机译：当前，基于EEG的元音分类在其下一代心驱动型书写中的不断增长的应用中正变得越来越重要。本文提出了一种新颖的方法，以特定的三字母格式将语音表达的字母分类，其中第一个和最后一个字母代表两个元音，中间是一个空格，其中没有字符可以想象。这种格式仅使用七个元音就能帮助识别26个英文字母。为了消除由并行思想引起的噪声渗透，我们使用了一种专用的神经模糊分类器，其中通过模糊逻辑实现的分类器的第一层消除了由于侧面有源信道干扰而可能引起的噪声蠕变。使用了两种模糊预处理模型。第一个模型是用1型模糊逻辑实现的，而第二个模型是用区间2型模糊集实现的。后一种模型可以照顾到人际和人际层面的不确定性。进行的实验表明，所提出的2型模糊分类器的性能大大优于1型分类器和传统的神经分类器。

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