کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
453680 694993 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification
ترجمه فارسی عنوان
بهینه سازی کانال و استخراج ویژگی های غیر خطی برای سیگنال الکتروانسفالوگرام طبقه بندی یک ؟؟
کلمات کلیدی
تشخیص سطح اطمینان، الکتروانسفالوگرام، انتخاب کانال مطلوب، استخراج ویژگی، طبقه بندی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی

In present work, a methodology for automatic vigilance level detection of human brain using nonlinear features of Electroencephalogram (EEG) signals is presented. Vigilance level detection methodology consists of three steps, EEG channels selection, feature extraction and classification. EEG signals obtained from 64 channels are sub-divided into four frequency sub-bands i.e. alpha, beta, delta and theta. Channel selection criteria Maximum Energy to Shannon Entropy ratio is applied on each frequency band to select appropriate EEG channels. EEG signals obtained from selected channels are further divided into frequency sub-bands i.e. alpha, beta and alpha–beta bands. Three nonlinear features such as Higuchi fractal dimension, Petrosian fractal dimension and Detrended Fluctuation Analysis are calculated to prepare three feature vectors respective to each frequency sub-bands. Three machine learning techniques are used for vigilance level detection such as Support Vector Machine, Least Square-Support Vector Machine and Artificial Neural Network.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 45, July 2015, Pages 222–234
نویسندگان
, , , , ,