METHODOLOGY


There are two methods of studying brain computer interfacing. One is the invasive other is noninvasive. In the invasive technique the Electrodes are directly implanted in the brain. This process is very complicated and very risky. But the signal strength is good and the noise in the signal will be reduced. Because of the risk this techniques has rarely used. The noninvasive technique of BCI is good for studying the EEG signals. In that technique the brain signals picked up by using the EEG electrodes placed on the head by using Cap of the EEG electrodes, noise in that signal is very high. Skin, skull, and power these noises are added in that signal, but by using the signal processing tools we can reduce that noise. For the studying the brain computer interfacing signal processing study is important. Because of signal processing understanding behavior of signal is easy. For study of signal processing many software tools available such as LABVIEW, MATLAB, and simulink. In this project MATLAB is used for the studying the EEG signal behavior. The information about the tool is given below.

TOOLS:- 
 MATLAB It is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java®. You can use MATLAB for a range of applications, including signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. More than a million engineers and scientists in industry and academia use MATLAB, the language of technical computing.[1] 

Signal processing toolbox in MATLAB :
 Signal Processing Toolbox™ provides industry-standard algorithms for analog and digital signal processing (DSP). You can use the toolbox to visualize signals in time and frequency domains, compute FFTs for spectral analysis, design FIR and IIR filters, and implement convolution, modulation, resampling, and other signal processing techniques. Algorithms in the toolbox can be used as a basis for developing custom algorithms for audio and speech processing, instrumentation, and baseband wireless communications. Matlab functions for the signal processing toolbox are as follows Signal generation, signal measurement, convolution and correlation, transforms, analog and digital filters, digital filter design, digital filter analysis, digital filter implementation, spectral resolution, parametric and linear prediction.  By using signal generation, signal measurement functions we can generate signal, measure signal. Convolution and correlation functions are used to calculating the convolution and correlation of the signal. Transforms functions used for the calculating Chirp z-Transform, Discrete Cosine Transform, Hilbert Transform, Walsh–Hadamard Transform, Discrete Fourier Transform, FFT-Based Time-Frequency Analysis, Cepstrum Analysis, Complex Cepstrum — Fundamental Frequency Estimation , Analytic Signal for Cosine, Envelope Extraction Using The Analytic Signal. Analog and digital filters functions used for the designing Analog Filters, Digital Filter Design, Digital Filter Analysis, and Digital Filter Implementation. Spectral Analysis functions are used for the Nonparametric and parametric spectral estimation, high resolution spectral estimation of the signal. Multirate Signal Processing functions are used for the Downsampling, upsampling, resampling, anti-aliasing filter, interpolation, decimation of the signal.[1]  

Technique  
Fourier transforms technique used for the signal analysis of the EEG signal. Generally EEG signal recorded in the time domain function, Fourier transform used for the detecting frequency components from signal.

References:
[1] http://www.physionet.org/pn4/eegmmidb/

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