Music generation based on emotional EEG

Luo, Gang; Chen, Hao; Li, Zhengxiu; Wang, Mingxun

doi:10.1145/3529466.3529492

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…This approach not only expands the sample size but also allows deep learning algorithms to extract richer information, achieving commendable results in emotion recognition studies. However, the suitability of this method for more complex cognitive activities [ 43 , 44 ], such as those involved in musical creativity, requires further experimental validation. The present study selected individuals with the highest recognition rates from two groups of subjects.…”

Section: Resultsmentioning

confidence: 99%

“…The framework of the SSAMAN is shown in Figure 3. In the SSAM model, the preprocessed, collected signals are first decomposed into four frequency bands most relevant to cognitive activities: the θ band (4-7 Hz), α band (8-13 Hz), β band (14-30 Hz), and γ band (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). A feature extraction module and a feature mapping module are then employed to convert the extracted feature sequences.…”

Section: Proposed Ssam Network Frameworkmentioning

confidence: 99%

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Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition

Gu,

Jiang,

Chen

et al. 2024

Brain Sciences

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In recent years, the integration of brain–computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between ‘recall and creation’ states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain’s ability to work in coordination but also provides crucial guidance for the advancement of brain–computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Ssam Network Frameworkmentioning

confidence: 99%

Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition

Gu,

Jiang,

Chen

et al. 2024

Brain Sciences

Self Cite

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“…It has been widely approved that kernel-based SVM has ensured better representation for samples and robustness. Thus, kernel-based SVM has been popular in MER (Thammasan et al, 2017a ; Avramidis et al, 2021 ; Luo et al, 2022 ). Other machine learning methods like random forest (Pisipati and Nandy, 2021 ) and KNN (Bhatti et al, 2016 ) were also developed for MER.…”

Section: Eeg-based Music Emotion Recognitionmentioning

confidence: 99%

“…LSTM was born for time series data since it can keep track of arbitrary long-term dependencies in the input sequences. Luo et al ( 2022 ) utilizes LSTM for sequence generation in his work. Additionally, deep learning algorithms overcome a troublesome and controversial problem, namely feature extraction, which liberates researchers from handcrafted feature selection to a certain extent.…”

Section: Eeg-based Music Emotion Recognitionmentioning

confidence: 99%

A review: Music-emotion recognition and analysis based on EEG signals

Cui

et al. 2022

Front. Neuroinform.

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Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

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