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

Cui, Xu; Wu, Yongrong; Wu, Jingli; You, Zhiyu; Xiahou, Jianbing; Ouyang, Menglin

doi:10.3389/fninf.2022.997282

Cited by 9 publications

(5 citation statements)

References 92 publications

(102 reference statements)

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“…In the context of machine learning, the establishment of distinct states for emotions serves as a significant framework for effectively addressing the challenge of emotion recognition. Numerous algorithms for music emotion identification based on machine learning have been proposed in the literature, with applications spanning composition and psychotherapy (Eerola and Vuoskoski, 2012 ; Cui et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Music-evoked emotions classification using vision transformer in EEG signals

Wang,

Lian,

Cheng

et al. 2024

Front. Psychol.

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IntroductionThe field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty.MethodsIn this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions.ExperimentsSubsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials.DiscussionThe experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios.

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Section: Introductionmentioning

confidence: 99%

Music-evoked emotions classification using vision transformer in EEG signals

Wang,

Lian,

Cheng

et al. 2024

Front. Psychol.

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“…The impact of user engagement on purchase intentions has also been analyzed based on EEG data [39]. In addition, emotion classification based on the EEG data of participants while listening to music is an emerging area of research [40].…”

Section: Related Workmentioning

confidence: 99%

Emotion Classification Based on CWT of ECG and GSR Signals Using Various CNN Models

Dessai

Virani

2023

Electronics

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Emotions expressed by humans can be identified from facial expressions, speech signals, or physiological signals. Among them, the use of physiological signals for emotion classification is a notable emerging area of research. In emotion recognition, a person’s electrocardiogram (ECG) and galvanic skin response (GSR) signals cannot be manipulated, unlike facial and voice signals. Moreover, wearables such as smartwatches and wristbands enable the detection of emotions in people’s naturalistic environment. During the COVID-19 pandemic, it was necessary to detect people’s emotions in order to ensure that appropriate actions were taken according to the prevailing situation and achieve societal balance. Experimentally, the duration of the emotion stimulus period and the social and non-social contexts of participants influence the emotion classification process. Hence, classification of emotions when participants are exposed to the elicitation process for a longer duration and taking into consideration the social context needs to be explored. This work explores the classification of emotions using five pretrained convolutional neural network (CNN) models: MobileNet, NASNetMobile, DenseNet 201, InceptionResnetV2, and EfficientNetB7. The continuous wavelet transform (CWT) coefficients were detected from ECG and GSR recordings from the AMIGOS database with suitable filtering. Scalograms of the sum of frequency coefficients versus time were obtained and converted into images. Emotions were classified using the pre-trained CNN models. The valence and arousal emotion classification accuracy obtained using ECG and GSR data were, respectively, 91.27% and 91.45% using the InceptionResnetV2 CNN classifier and 99.19% and 98.39% using the MobileNet CNN classifier. Other studies have not explored the use of scalograms to represent ECG and GSR CWT features for emotion classification using deep learning models. Additionally, this study provides a novel classification of emotions built on individual and group settings using ECG data. When the participants watched long-duration emotion elicitation videos individually and in groups, the accuracy was around 99.8%. MobileNet had the highest accuracy and shortest execution time. These subject-independent classification methods enable emotion classification independent of varying human behavior.

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“…The therapeutic and joyous effect that music has on individuals can be linked to the fact that both music and human physiology are governed by similar natural laws [ 6 ]. The technique of mapping brain activity to music by utilizing EEG signals has been demonstrated to be an effective approach [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 9 publications

References 92 publications

Music-evoked emotions classification using vision transformer in EEG signals

Music-evoked emotions classification using vision transformer in EEG signals

Emotion Classification Based on CWT of ECG and GSR Signals Using Various CNN Models

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

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