Eeg Signal Classification Based on Nn with Ica and Stft
ثبت نشده
چکیده
A novel approach is proposed for Electroencephalogram signal classification using Artificial Neural Network based on Independent Component Analysis and Short Time Fourier Transform. The source EEG signals contain the electrical activity of the brain produced in the background by the cerebral cortex nerve cells. EEG is one of the most utilized methods for effective analysis of the brain functions. The accuracy of the EEG signal classification technique from the previous works is enhanced. FastICA is used as a preprocessing step, while STFT is used for adequate denoising of the EEG signal.
منابع مشابه
An ImprovedEEG Signal Classification Using Neural Network with the Consequence of ICA and STFT
Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizur...
متن کاملClassification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal
The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملComparison of MLP NN Approach with PCA and ICA for Extraction of Hidden Regulatory Signals in Biological Networks
The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as PCA and ICA have been employed for computing low-dimensional or hidden represe...
متن کاملPerformance Analysis of Epileptic Seizure Detection Using DWT & ICA with Neural Networks
The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is compare the automatic detection of EEG patterns using Discrete wavelet...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015