The coordination of EEG rhythms between brain areas can be detected by using Coherence analysis. Is a method developed on the base of classic coherence analysis and signals joint time-frequency representations in recent years. It was used to extract transient characteristics of interactions among brain areas. Describes the temporal, spatial and frequency relationships of brain activities. In this...
How to extract brain wave bands: Gamma, Beta, Alpha, Theta, Delta
All human beings display five different types of electrical patterns or brain waves over the cortex in order of highest frequency to lowest are as follows: Gamma, Beta, Alpha, Theta, and Delta. Each brain wave has a purpose and helps serve us in optimal mental functioning. If one of the five types of brain waves are either overproduced or under-produced in our brain, it can cause problems [1]...
Fast Kalman Filter in Matlab
Kalman filtering-smoothing is a fundamental tool in statistical time series analysis: it implements the optimal Bayesian filter in the linear-Gaussian setting, and serves as a key step in the inference algorithms for a wide variety of nonlinear and non-Gaussian models. However, using this kind of filter in small embedded systems is not a good choice due to the computational intensive maths. For...
How to localize EEG-signals according to the international 10-20 system of electrodes position
In order to perform measurements for feature extraction, it is needed to localize the channels from the data-set recordings to, which contain the raw brain waves with some technical specification of each signal. CHB-MIT Scalp EEG Database is one of the most cited resources used in prediction detection experiments. It is also one of the few publicly available invasive EEG data-sets. The database...
Edge detection with a given direction using Matlab
The aim of this article is to detect the edges with a given direction in an image. To that end create a function [ E ] = oriented_edges( I, thr, a, da ) that takes as input a double grayscale image Ι, a threshold value thr, a direction a, and an angle da. The output of the function is a binary image Ε where the pixels that meet the following requirements should have the value 1: The pixel...
Principal Component Analysis using Matlab
PCA is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Below are the steps of the algorithm: close all; % clear all; clc; % data = load('Data/eeg.mat'); % data = data.data{1,2}; Step 1 – Initialize the dataset, 6 vectors of 32 sample data % % Step 1 - Initialize the dataset, 6 vectors of 32 sample data % X =...
