Identifying Stable Patterns over Time for Emotion Recognition from EEG

01/10/2016
by   Wei-Long Zheng, et al.
0

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.

READ FULL TEXT

page 5

page 7

page 9

page 11

research
09/13/2022

Weight-based Channel-model Matrix Framework: a reasonable solution for EEG-based cross-dataset emotion recognition

Cross-dataset emotion recognition as an extremely challenging task in th...
research
08/29/2017

Gender and Emotion Recognition with Implicit User Signals

We examine the utility of implicit user behavioral signals captured usin...
research
10/30/2020

Multiscale Fractal Analysis of Stimulated EEG Signals with Application to Emotion Classification

Emotion Recognition from EEG signals has long been researched as it can ...
research
03/05/2021

Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games

Human emotion recognition has become a need for more realistic and inter...
research
05/01/2019

A new model for the implementation of positive and negative emotion recognition

The large range of potential applications, not only for patients but als...
research
11/25/2021

Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism

Current models on Explainable Artificial Intelligence (XAI) have shown a...
research
09/26/2017

Emotion-Recognition Using Smart Watch Accelerometer Data: Preliminary Findings

This study investigates the use of accelerometer data from a smart watch...

Please sign up or login with your details

Forgot password? Click here to reset