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Coherence analysis can detect the coordination of EEG rhythms between brain areas. 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 analysis discuss the frequency-varying coherence of EEG (Electroencephalogram) to examine the coordination mechanism of the brain. The power spectral density…

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]. For this reason, it is important to…

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 that reason there are several variations of the original Kalman filter which may…

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. The 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 contains 24 hour-long continuous pre-surgical invasive EEG recordings of 22 patients…

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:…

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:

Step 1 – Initialize the dataset, 6 vectors of 32 sample data

Step 2 – Subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. [math]Y= X – (O * Mean(X)) [/math], where [math]O…