Neuro-fuzzy classifier to recognize mental tasks in a BCI
作者： Luis D Lledo , Jose M Cano , Andres Ubeda , Eduardo Ianez , Jose M Azorin
DOI: 10.1109/BIOROB.2012.6290302
关键词: Fast Fourier transform 、 Electroencephalography 、 Brain–computer interface 、 Artificial intelligence 、 Machine learning 、 Neuro fuzzy classifier 、 Pattern recognition 、 Classifier (UML) 、 Computer science 、 Feature extraction 、 Fuzzy neural nets 、 Brain activity and meditation
摘要: This paper presents the first results of online classification using a model based on neuro-fuzzy architecture called S-dFasArt, in order to recognize real time and with sufficient reliability between two mental tasks Brain Computer Interface (BCI). Spontaneous brain activity recorded non-invasive techniques processed through Fast Fourier Transform (FFT) has been used test classifier. The dynamic characteristics ability algorithm make it very suitable interpret EEG signals. classifier designed is creation combination diferent models alterning sessions signals adjustment phase performing complete study find best values parameters. In paper, each described. New voting strategies levels uncertainty have incorporated improve success rate classification. experimental different users reported.
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