Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG

Abstract

Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

“…E MOTION is a psychological and physiological response formed by sensing external and internal stimuli that influences human behavior and plays a significant role in daily life [1], [2]. As one of the most important research topics in affective computing, emotion recognition has garnered increasing interest in recent years due to its wide range of potential applications in human-computer interaction [3], disease detection [4]- [6], fatigue driving [7]- [10], mental workload estimation [11]- [14], and cognitive neuroscience [15]- [19].…”

Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG

“…Shannon first introduced the concept of information entropy based on thermodynamic entropy to describe the distribution of signal components. Up to now, many entropy algorithms have been proposed, mainly including ApEn [ 34 , 35 ], SampEn [ 36 , 37 ], Permutation entropy (PE) [ 38 ], Fuzzy entropy (FuzzyEn) [ 39 ], Shannon Wavelet entropy (SWE) [ 40 ], Hilbert-Huang spectral entropy (HHSE) [ 41 ], and multi-scale entropy (MSE) [ 42 ]. ApEn and SampEn are based on the time series, while above other methods are based on the frequency spectrum.…”