Affective content analysis of music emotion through EEG

Hsu, Jia-Lien; Zhen, Yan-Lin; Lin, Tzu‐Chieh; Chiu, Yi-Shiuan

doi:10.1007/s00530-017-0542-0

Cited by 37 publications

(15 citation statements)

References 36 publications

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“…Therefore, studies have used biophysical signals or metrics (such as electromyography and skin conductance [43]) to recognize user emotions. Some studies exploit the technologies used in cognitive neuroscience to represent user emotion states through electroencephalography (EEG) [44,45] in music-related activities. Ayata et al even built an emotion-based music recommendation system by tracking user emotion states with wearable physiological sensors [17].…”

Section: Related Workmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

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Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

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Section: Related Workmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

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“…There was also musical condition, and positive mood was elicited by La Primavera (Spring) from The Four Seasons by Vivaldi [21]. Elicitation of emotion by music is also very common [28] and reveals that mood influences attentional networks [23], or evokes genuine basic emotions such as happiness or sadness [10].…”

Section: Mood Induction and Measuresmentioning

confidence: 99%

Effects of individual’s emotions on saliency and visual search

et al. 2020

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Psychological studies have already proven a connection between emotional stimuli and visual attention. However, there is a lack of evidence of how much an individual's mood influences visual information processing of emotionally neutral stimuli. In contrast to prior studies, we explored the effect of positive mood on bottom-up low-level visual saliency. In our extensive experimental studies, we induced positive or neutral emotions in 106 subjects using a psychological method of autobiographical memories and listening to positive music and then performed an eye-tracking study with three task typesfree viewing, memorizing the image content and visual search tasks. We explored differences in the human gaze behaviour between both emotions and relate them to the bottom-up attention correlation between emotionally aroused and non-aroused subjects. We observed that positive emotions induce a strong broadening and engagement effect on the bottom-up attention while performing all task types. On the other hand, we found out that certain tasks are solved less efficiently when experiencing a positive mood. Therefore, we claim that positive mood may also act as a distractor while performing certain types of tasks.

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“…The frameworks of emotion analysis based on EEG-multimodal data fusion have similarity among existing studies. Affective images, music [50], videos [51]- [57] have been selected as the stimuli to induce emotions. Users' emotion states are measured by physiological and EEG sensors and the emotion annotations are rated by users in the experiment accordingly.…”

Section: Emotion Recognition Based On Eeg-multimodal Data Fusionmentioning

confidence: 99%

“…In the recent study performed by Jia et al in 2018, music features and EEG features were combined to fuse the dataset and build the personalized model for music emotion recognition, which achieved an average mse of 0.176 [50]. L. Granados et al applied the deep learning approach on the EEG and galvanic skin response dataset and achieved a classification accuracy of 71% for arousal and 75% for valence [59].…”

Section: Emotion Recognition Based On Eeg-multimodal Data Fusionmentioning

confidence: 99%

Exploiting EEG Signals and Audiovisual Feature Fusion for Video Emotion Recognition

et al. 2019

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External stimulation, mood swing, and physiological arousal are closely related and induced by each other. The exploration of internal relations between these three aspects is interesting and significant. Currently, video is the most popular multimedia stimuli that can express rich emotional semantics by its visual and auditory features. Apart from the video features, human electroencephalography (EEG) features can provide useful information for video emotion recognition, as they are the direct and instant authentic feedback on human perception with individuality. In this paper, we collected a total of 39 participants' EEG data induced by watching emotional video clips and built a fusion dataset of EEG and video features. Subsequently, the machine-learning algorithms, including Liblinear, REPTree, XGBoost, MultilayerPerceptron, RandomTree, and RBFNetwork were applied to obtain the optimal model for video emotion recognition based on a multi-modal dataset. We discovered that using the data fusion of all-band EEG power spectrum density features and video audio-visual features can achieve the best recognition results. The video emotion classification accuracy achieves 96.79% for valence (Positive/Negative) and 97.79% for arousal (High/Low).The study shows that this method can be a potential method of video emotion indexing for video information retrieval.

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Affective content analysis of music emotion through EEG

Cited by 37 publications

References 36 publications

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Effects of individual’s emotions on saliency and visual search

Exploiting EEG Signals and Audiovisual Feature Fusion for Video Emotion Recognition

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