Neural network technology for pattern recognition, stock prediction and market forecasting


Pattern recognition

DCT-ANN Face Identification

Wavelet-ANN Face Recognition

Text-Independent Speaker Recognition based on ANN

Assembler-based Neural Network Simulator

Facial Expression Recognition System

Iris Recognition Based on Neural Networks

Neural Networks Based Signature Recognition

Eye Detection Based Facial Expression Recognition

Gait Recognition System

Leaf Recognition System

Optical Character Recognition

Neural Network Fingerprint Recognition

Keystroke Recognition

EEG Recognition

Neural Network Speech Recognition

Image processing

Image Compression With Neural Networks

Stock Market Forecasting

Neural Network Forecasting

External resources

Advanced Source Code .Com

Genetic Algorithms .It

Face Recognition .It

Iris Recognition .It

EEG Recognition System

Download now Matlab source code
Requirements: Matlab, Matlab System Identification Toolbox, Matlab Neural Network Toolbox.

Functional brain imaging techniques that are designed to measure an aspect of brain function can be employed to obtain tangible information related to brain activity. Electroencephalogram (EEG) is one such technique, which measures the electric fields that are produced by the activity in the brain. From EEG measurements, it is possible to extract information and determine the intent of the user for a number of different mental activities (e.g., motor imagery, motor planning), using a variety of electrophysiological signals such as slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. The use of EEG for the communication of intent is one of the bases of Brain-Computer Interface (BCI) research, which is geared towards the development of systems to afford people with disabilities or severe neuromuscular disorders the capability of basic communication (by operating word processing programs or through neuroprostheses). EEG signals acquired during mental activities can also be used for subject identification to create a more secure environment for applications such as BCIs, game play, or silent communication.

Code has been successfully tested on UCI EEG Database. This database contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-msec epoch) for 1 second.

Index Terms: Matlab, source, code, EEG, recognition, electroencephalogram, brain, electric, field.

Release 1.0 Date 2012.06.18
Major features:

Neural Networks . It Luigi Rosa mobile +39 3207214179