A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Author(s):  
Jaideep Singh Sachadev ◽  
Roheet Bhatnagar
Author(s):  
Jing Chen ◽  
Haifeng Li ◽  
Lin Ma ◽  
Hongjian Bo

Emotion detection using EEG signals has advantages in eliminating social masking to obtain a better understanding of underlying emotions. This paper presents the cognitive response to emotional speech and emotion recognition from EEG signals. A framework is proposed to recognize mental states from EEG signals induced by emotional speech: First, speech-evoked emotion cognitive experiment is designed, and EEG dataset is collected. Second, power-related features are extracted using EEMD-HHT, which is more accurate to reflect the instantaneous frequency of the signal than STFT and WT. An extensive analysis of relationships between frequency bands and emotional annotation of stimulus are presented using MIC and statistical analysis. The strongest correlations with EEG signals are found in lateral and medial orbitofrontal cortex (OFC). Finally, the performance of different feature set and classifier combinations are evaluated, and the experiments show that the framework proposed in this paper can effectively recognize emotion from EEG signals with accuracy of 75.7% for valence and 71.4% for arousal.


2016 ◽  
Vol 28 (11) ◽  
pp. 3153-3161 ◽  
Author(s):  
Yong Zhang ◽  
Xiaomin Ji ◽  
Bo Liu ◽  
Dan Huang ◽  
Fuding Xie ◽  
...  

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