EEG-Based Emotion Recognition in Music Listening
By Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, J. Duann, Jyh-Horng Chen. IEEE Transactions on Biomedical Engineering, 2010
961 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/eca7a76e09679c5d9d44349ae3073aa18bb49460
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Experimental and Cognitive Psychology
Citations in the corpus
- Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “4% like/dislike [50] Offline Both SVM RBF 86.”
- EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “The majority of existing features are computed on a single channel, e.g., statistical features [26], PSD [27], differential entropy (DE) [8], and wavelet features [28].”
- Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, methodology): “Therefore, the conventional method is to train new models for new persons [8], [11].”
- EEG-based classification of positive and negative affective states (2014, background): “[4,18,19] Studies on emotion recognition commonly draw on two primary emotional”
- Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, background): “and accuracy rate in the literature [24], [40], [42].”
- EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “[10] evaluated the emotion-specific features based on the power spectral changes of EEG and assessed the association between”
- Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, methodology): “[10] Music 24 Power spectral density and asymmetry features of five frequency bands, 26 participants, evaluated SVM Joy, anger, sadness, and pleasure 82.”
- EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, background): “existing studies have shown the effectiveness and feasibility of EEG [14], [27], [31], [32].”
- A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “In the work of [29,93], a 512-point STFT was presented to extract spectrogram from 30 channels of the EEG.”
- Feature Extraction and Selection for Emotion Recognition from EEG (2014, methodology): “used the ratio of band powers in their study [33].”
- EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “using EEG signals [115], [116] include joy, sadness, surprise, anger, fear, and disgust, while the emotions could also be simply categorised in a binary classification as positive or negative [117].”
- EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities (2020, background): “Researchers have highlighted the significant challenges posed by intersubject classification in affective computing [140, 142–147].”
- EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, background): “the differential asymmetry (DASM) feature [24], the rational asymmetry (RASM) feature [32] and the differential caudality (DCAU) feature [19].”
- Emotional state classification from EEG data using machine learning approach (2014, background): “[8] J. Coan, J. Allen, Frontal EEG asymmetry as a moderator and mediator of emotion, Biol.”
- A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, background): “[4] investigated the relationships between emotional states and brain activities, and extracted PSD, differential asymmetry power, and rational asymmetry power as features.”