Emotion classification based on gamma-band EEG

By Mu Li, Bao-Liang Lu. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009

433 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/68f20fa8b600ff109b554391e38fe9b477445c21

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Emotions Recognition Using EEG Signals: A Survey (2019, background): “[29] Band pass frequency filter with a bandwidth of 4-45 Hz; laplacian reference [40] Band pass frequency filter with a bandwidth of 4-45 Hz; laplacian reference [34] Remove segments with EOG [42] Band pass frequency filter with a bandwidth of 1-100 Hz [43] Band pass frequency filter with a bandwidt”
EEG-based emotion classification using deep belief networks (2014, background): “Previous researches have shown that higher frequency brain activity reﬂects emotional and cognitive processes [8] [9].”
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, background): “This observation is consistent with our previous finding [22], [32], [33].”
EEG-based classification of positive and negative affective states (2014, background): “This is in line with [49-51], where it has been shown that the gamma band is suitable for EEG-based emotion classification.”
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, result): “These results gave a further verification on our previous work [71].”
EEG-based emotion recognition during watching movies (2011, background): “Li and Lu confirmed that gamma band also played a crucial part in this field [8].”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “For example, Li and Lu (2009) examined the recognition performance using ERD/ERS features extracted from diﬀerent frequency bands and found that 43.5–94.5 Hz, the higher gamma band, was the optimal frequency band related to happiness and sadness.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, positive): “Mu Li and Bao-Liang Lu (2009) utilized EEG signals for emotion recognition in response to emotional pictures.”
Feature Extraction and Selection for Emotion Recognition from EEG (2014, methodology): “The mostly used algorithm to compute discrete fourier transform (DFT) is the fast fourier transform (FFT) applied by [3], [7], [12], [29], [30], [31], [32].”
Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “bands, which imply that beta and gamma oscillations of brain activity are more related to the processing of these three emotional states than other frequency oscillations, as described in [55]–[57].”
EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “Earlier traditional machine learningbased researches have reported that the features extracted from high-frequency bands (the Gamma and Beta bands) are more effective for an algorithm to recognize both Valence and Arousal dimensions [59].”
Emotional state classification from EEG data using machine learning approach (2014, methodology): “[42] used EEG signals to recognize emotion in response to emotional pictures.”
EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, methodology): “In addition, EEG signals are widely used for emotion analysis because EEG can be explored various information about emotions from frequency band, electrode position, and temporal information [13], [14], [15].”