EEG-based emotion recognition during watching movies

By Dan Nie, Xiao-Wei Wang, Li-Chen Shi, Bao-Liang Lu. International IEEE/EMBS Conference on Neural Engineering, 2011

373 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/38757e06fdff9abf0997a7e69ffcd31a22ebd3af

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Citations in the corpus

FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals (2019, background): “Due to the fact that rPPG is mostly evaluated by the accuracy in heart rate, we researched other features for image authenticity [34], classiﬁcation of EEG signals [28, 41], statistical analysis [60, 50, 25], and emotion recognition [39, 41].”
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, background): “tion from EEG signals can achieve a higher classification accuracy [9].”
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): “[29] reported that the participant-independent features associated with positive and negative emotions are mainly in the right occipital lobe and parietal lobe for the alpha band, the central site for the beta band, and the left frontal lobe and right temporal lobe for the gamma band.”
A Review on the Computational Methods for Emotional State Estimation from the Human EEG (2013, positive): “Our model may provide an effectiveway for online emotion aware computing, especially when we need to keep track of changes in the emotional state from EEG measurements continuously over time, for instance, tracking emotional changes while a subject is watching movies [95].”
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, background): “, d band (1-3 Hz), u band (4-7 Hz), a band (8-13 Hz), b band (14-30 Hz) and g band (31-50 Hz) [21], [26], [27], [28], and then extract EEG features from each frequency band, respectively.”
EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation (2014, neutral): “Moreover, several studies [8–11] used PSD of EEG data as the input features and performed emotion classification by using SVM.”
An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals (2020, methodology): “by using all the electrodes on the scalp [35].”
A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “Therefore, FFT was adopted to calculate the spectrogram of the EEG channels [91,92].”
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, background): “Since some EEG channels are irrelevant to emotion recognition [57], these irrelevant channels need more computational cost, introduce noise to emotion recognition, and degrade the performance of trained models.”
Emotional state classification from EEG data using machine learning approach (2014, neutral): “187.”
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, background): “3 Motivated by our previous findings of critical brain areas for emotion recognition [55], [63], we selected six symmetrical temporal electrodes above the ears, which are FT7, FT8, T7,”
Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking (2013, background): “…work in Min et al. (2003) and Potts and Tucker (2001), and the association between h and a from the left frontal regions and stated preferences was established in several studies (Custdio, 2010; Kawasaki & Yamaguchi, 2012; Nie et al., 2011; Yokomatsu, Ito, Mitsukura, Jianting, & Fukumi, 1720).”
Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “[58] Video (4 min) 6 (3/3) Positive and negative”