Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Autor:	Aya Samaha, Khaled Alkamha, Emad A. Awada, Mohammad H. Alomari
Rok vydání:	2014
Předmět:	Artificial neural network medicine.diagnostic_test business.industry Computer science Feature extraction Electroencephalography Wavelet transform analysis Wavelet Coiflet Mean absolute value medicine Pharmacology (medical) Computer vision Artificial intelligence business Classifier (UML)
Zdroj:	Computer and Information Science. 7
ISSN:	1913-8997 1913-8989
DOI:	10.5539/cis.v7n2p17
Popis:	Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into either fists or feet. Wavelet families such as Daubechies, Symlets, and Coiflets wavelets were used to analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients. Intensive NN training and testing experiments were carried out and different network configurations were compared. The optimum classification performance of 89.11% was achieved with a NN classifier of 20 hidden layers while using the Mean Absolute Value (MAV) of the Coiflets wavelet coefficients as inputs to NN. The proposed system showed a good performance that enables controlling computer applications via imagined fists and feet movements.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2cc8bb0e074ccef8145a58687951b0f0 https://doi.org/10.5539/cis.v7n2p17 Zobrazit plný text záznamu