A new approach to recognition of human emotions using brain signals and music stimuli

Er, Mehmet Bilal; Çiğ, Harun; Aydilek, İbrahim Berkan

doi:10.1016/j.apacoust.2020.107840

Cited by 19 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Convolution Neuron Network (CNN) is well-suited to processing image data with the bias of transitional invariance. Thus, the power spectrogram generated by the EEG frequency signal was adopted reasonably (Er et al, 2021 ; Liu et al, 2022 ). LSTM was born for time series data since it can keep track of arbitrary long-term dependencies in the input sequences.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…The dancing robots' performance style must match the music style. In recent years, researchers have applied neural networks (NNs) to audio signal processing (ASP) (Jiang, 2020 ; Er et al, 2021 ). Over time, many variants of RNN have been developed and applied to ASP.…”

Section: Design Of Music Style Recognition Model and Construction Of ...mentioning

confidence: 99%

Recognition of musical beat and style and applications in interactive humanoid robot

Chu

2022

Front. Neurorobot.

View full text Add to dashboard Cite

The musical beat and style recognition have high application value in music information retrieval. However, the traditional methods mostly use a convolutional neural network (CNN) as the backbone and have poor performance. Accordingly, the present work chooses a recurrent neural network (RNN) in deep learning (DL) to identify musical beats and styles. The proposed model is applied to an interactive humanoid robot. First, DL-based musical beat and style recognition technologies are studied. On this basis, a note beat recognition method combining attention mechanism (AM) and independent RNN (IndRNN) [AM-IndRNN] is proposed. The AM-IndRNN can effectively avoid gradient vanishing and gradient exploding. Second, the audio music files are divided into multiple styles using the music signal's temporal features. A human dancing robot using a multimodal drive is constructed. Finally, the proposed method is tested. The results show that the proposed AM-IndRNN outperforms multiple parallel long short-term memory (LSTM) models and IndRNN in recognition accuracy (88.9%) and loss rate (0.0748). Therefore, the AM-optimized LSTM model has gained a higher recognition accuracy. The research results provide specific ideas for applying DL technology in musical beat and style recognition.

show abstract

“…The most widely applied architecture in deep learning is convolutional neural networks (CNN) architecture. Among the studies using CNN architecture, Er et al [15] performed two-channel multi-class discrete emotion recognition from EEG signals using pre-trained deep learning networks: AlexNet and VGG16. The authors obtained EEG signals from nine participants who regularly listened to Turkish Art, Turkish Folk, Turkish Pop, and Turkish Jazz Music.…”

Section: Related Workmentioning

confidence: 99%

GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

Toraman¹,

Dursun²

2021

View full text Add to dashboard Cite

Human emotion recognition with machine learning methods through electroencephalographic (EEG) signals has become a highly interesting subject for researchers. Although it is simple to define emotions that can be expressed physically such as speech, facial expressions, and gestures, it is more difficult to define psychological emotions that are expressed internally. The most important stimuli in revealing inner emotions are aural and visual stimuli. In this study, EEG signals using both aural and visual stimuli were examined and emotions were evaluated in both binary and multi-class emotion recognitions models. A general emotion recognition model was proposed for non-subject-based classification. Unlike in previous studies, a subject-based testing was performed for the first time in the literature. Capsule Networks, a new neural network model, has been developed for binary and multi-class emotion recognition. In the proposed method, a novel fusion strategy was introduced for binary-class emotion recognition and the model was tested using the GAMEEMO dataset. Binary-class emotion recognition achieved a classification accuracy which was 10% better than the classification performance achieved in other studies in the literature. Based on these findings, we suggest that the proposed method will bring a different perspective to emotion recognition.

show abstract

A new approach to recognition of human emotions using brain signals and music stimuli

Cited by 19 publications

References 35 publications

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

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

Recognition of musical beat and style and applications in interactive humanoid robot

GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

Contact Info

Product

Resources

About