Study on Arousal Recognition Method Using Electroencephalogram ( EEG ) Signals

نویسندگان

  • Yujun NIU
  • Ichiro Yamada
چکیده

Improving arousal recognition accuracy based on EEG signals is important for emotion recognition. In this research, discrete wavelet transform was employed to extract features and cross-level method was proposed to select effective features. Cross-level method showed a great potential for 2-level arousal classification and the recognition accuracy reached to 91.8%. Besides, sensitivity of EEG channels is also discussed based on two ranking methods of SCP (single-channel performance) and ANOVA (analysis of variance). Finally, arousal recognition method based on EEG signals is applied for constructing Japanese emotion database.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods

Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video we...

متن کامل

Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods

Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video we...

متن کامل

A hybrid EEG-based emotion recognition approach using Wavelet Convolutional Neural Networks (WCNN) and support vector machine

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...

متن کامل

Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

متن کامل

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emot...

متن کامل

ذخیره 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 2014