Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol 3, No.3, 2012 10 A Novel Neural Network Classifier for Brain Computer Interface Aparna Chaparala 1* Dr. J.V.R.Murthy 2 Dr. B.Raveendra Babu 3 M.V.P.Chandra Sekhara Rao 1 1. R.V.R.&J.C. College of Engineering, Guntur - 522019, AP, India 2. Dept. of CSE, JNTU College of Engineering, Kakinada, AP, India 3. DELTA Technology & Management Services Pvt. Ltd., Hyderabad, AP, India * E-mail of the corresponding author: chaparala_aparna@yahoo.com Abstract 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 polynomial 1. Introduction A Brain Computer Interface (BCI) system records the brain signals through Electro-encephalography (EEG), preprocesses the raw signals to remove artifacts and noise, and employs various signal processing algorithms to translate patterns into meaningful control commands. The purpose of BCI is to control devices like computers, speech synthesizers, assistive appliances and neural prostheses by individual with severe motor disabilities, through brain signals. Signal processing plays an important role in BCI system design, as meaningful patterns are to be extracted from the brain signal. Figure 1 depicts a generic BCI system (Mason S G et al. 2003). The device is controlled through a series of functional components. Electrodes record signals from the users scalp and convert the signals into electrical signals which are amplified. The artifact processor removes the artifacts from the amplified signals. Feature generator transforms the signals into feature values that are the base for the control of device. The feature generator is generally made up of three steps, signal enhancement, feature extraction and dimensionality reduction. Signal enhancement refers to the preprocessing of the signals to increase the signal-to-noise ratio of the signal. Most commonly used preprocessing methods are Surface Laplacian (Mc Farland D et al. 1998 ; Dornhege G et al. 2004), Independent Component Analysis (ICA) (Serby H et al. 2005), and Principal Component Analysis (Guan J et al. 2005). Feature extraction generates the feature vectors and dimensionality reduction, reduces the number of feature. Thus features useful for classification is identified and chosen while artifacts and noise are eliminated in feature generator step. Genetic algorithm (Peterson D A et al. 2005), PCA (Bashashati A et al. 2005), Distinctive sensitive learning vector quantization (DSLVQ)