A Novel Neural Network Classifier for Brain Computer Interface

Brain computer interfaces (BCI) provides a non-muscular channel for controlling a device through electroencephalographic signals to perform different tasks. The BCI system records the Electro-encephalography (EEG) and detects specific patterns that initiate control commands of the device. The efficiency of the BCI depends upon the methods used to process the brain signals and classify various patterns of brain signal accurately to perform different tasks. Due to the presence of artifacts in the raw EEG signal, it is required to preprocess the signals for efficient feature extraction. In this paper it is proposed to implement a BCI system which extracts the EEG features using Discrete Cosine transforms. Also, two stages of filtering with the first stage being a butterworth filter and the second stage consisting of an moving average 15 point spencer filter has been used to remove random noise and at the same time maintaining a sharp step response. The classification of the signals is done using the proposed Semi Partial Recurrent Neural Network. The proposed method has very good classification accuracy compared to conventional neural network classifiers. Keywords: Brain Computer Interface (BCI), Electro Encephalography (EEG), Discrete Cosine transforms(DCT), Butterworth filters, Spencer filters, Semi Partial Recurrent Neural network, laguarre polynomia

Synthesis And Characterization Of Co-Doped SnO2/TiO2 Semiconductor Nano Crystallites Via Sol-Gel Method

SnO2/TiO2 nano particles are novel wide band gap semiconductors with modified applications of SnO2 and TiO2 in some fields including gas sensing, photo catalytic, solar cells and so on. The Co-doped SnO2/TiO2 nano particles were obtained via sol-gel method with different amounts of doping material as 2.5 %, 6 % and 10 mol %. The crystallite sizes of resulting material were from 3.8 nm for 0.1 wt % Co-doped SnO2/TiO2 to 19.1 nm for un-doped. Morphology and nanostructure of the crystalline SnO2/TiO2 nano particles were characterized by means of X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), Thermal gravimetric analysis (TGA), field emission scanning electron microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDX). It has been shown that fine semiconductor nano structures were formed. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/933

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