کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11030113 | 1646387 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Emotion recognition using empirical mode decomposition and approximation entropy
ترجمه فارسی عنوان
شناخت احساسی با استفاده از تقسیم حالت تجربی و آنتروپی تقریبی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
شناخت احساسی، الکتروانسفالوگرام، طبقه بندی، تجزیه حالت تجربی، آنتروپی تقریبی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
Automatic human emotion recognition is a key technology for human-machine interaction. In this paper, we propose an electroencephalogram (EEG) feature extraction method that leverages empirical mode decomposition and Approximation Entropy. In our proposed method, Empirical Mode Decomposition (EMD) is used to process EEG signals after data processing and obtains several intrinsic eigenmode functions. The Approximation Entropy (ApEn) of the first four Intrinsic Mode Functions (IMFs) is computed, which is used as the features from EEG signals for learning and recognition. An integration of Deep Belief Network and Support Vector Machine is devised for classification, which takes the eigenvectors from the extracted feature to identify four principal human emotions, namely happy, calm, sad, and fear. Experiments are conducted with EEG data acquired with a 16-lead device. Our experimental results demonstrate that the proposed method achieves an improved accuracy that is highly competitive to the state-of-the-art methods. The average accuracy is 83.34%, and the best accuracy reaches 87.32%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 72, November 2018, Pages 383-392
Journal: Computers & Electrical Engineering - Volume 72, November 2018, Pages 383-392
نویسندگان
Tian Chen, Sihang Ju, Xiaohui Yuan, Mohamed Elhoseny, Fuji Ren, Mingyan Fan, Zhangang Chen,