ArticleScientific reports2019
Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 71 citations in OpenAlex.
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- A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning.Brain informatics · 2025Article
- Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.Scientific reports · 2025Article
- A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students.Diagnostics (Basel, Switzerland) · 2025Article
- Identifying relevant EEG channels for subject-independent emotion recognition using attention network layers.Frontiers in psychiatry · 2025Article
- On joy and sorrow: Neuroimaging meta-analyses of music-induced emotion.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement.Nature communications · 2024Article
- Emotional State Measurement Trial (EMOPROEXE): A Protocol for Promoting Exercise in Adults and Children with Cerebral Palsy.Journal of personalized medicine · 2024Article
- Frontal and cerebellar contributions to pitch and rhythm processing: a TMS study.Brain structure & function · 2024Article
- Mental stress recognition on the fly using neuroplasticity spiking neural networks.Scientific reports · 2023Article
- Neural decoding of music from the EEG.Scientific reports · 2023Article
- Inter-subject correlations of EEG reflect subjective arousal and acoustic features of music.Frontiers in human neuroscience · 2023Article
- Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG.Entropy (Basel, Switzerland) · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- The Neural Blending of Words and Movement: Event-Related Potential Signatures of Semantic and Action Processes during Motor-Language Coupling.Journal of cognitive neuroscience · 2021Article
- Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.Journal of medical systems · 2021Article
- Mathematical Modeling of Brain Activity under Specific Auditory Stimulation.Computational and mathematical methods in medicine · 2021Article
- Synaptic development of layer V pyramidal neurons in the prenatal human prefrontal neocortex: a Neurolucida-aided Golgi study.Neural regeneration research · 2020Article
- Context-dependent neural responses to minor notes in frontal and temporal regions distinguish musicians from nonmusicians.Cognitive, affective & behavioral neuroscience · 2020Article
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Authors and funding
6 authors at 4 institutions in 1 country.
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Abstract
The ability of music to evoke activity changes in the core brain structures that underlie the experience of emotion suggests that it has the potential to be used in therapies for emotion disorders. A large volume of research has identified a network of sub-cortical brain regions underlying music-induced emotions. Additionally, separate evidence from electroencephalography (EEG) studies suggests that prefrontal asymmetry in the EEG reflects the approach-withdrawal response to music-induced emotion. However, fMRI and EEG measure quite different brain processes and we do not have a detailed understanding of the functional relationships between them in relation to music-induced emotion. We employ a joint EEG - fMRI paradigm to explore how EEG-based neural correlates of the approach-withdrawal response to music reflect activity changes in the sub-cortical emotional response network. The neural correlates examined are asymmetry in the prefrontal EEG, and the degree of disorder in that asymmetry over time, as measured by entropy. Participants' EEG and fMRI were recorded simultaneously while the participants listened to music that had been specifically generated to target the elicitation of a wide range of affective states. While listening to this music, participants also continuously reported their felt affective states. Here we report on co-variations in the dynamics of these self-reports, the EEG, and the sub-cortical brain activity. We find that a set of sub-cortical brain regions in the emotional response network exhibits activity that significantly relates to prefrontal EEG asymmetry. Specifically, EEG in the pre-frontal cortex reflects not only cortical activity, but also changes in activity in the amygdala, posterior temporal cortex, and cerebellum. We also find that, while the magnitude of the asymmetry reflects activity in parts of the limbic and paralimbic systems, the entropy of that asymmetry reflects activity in parts of the autonomic response network such as the auditory cortex. This suggests that asymmetry magnitude reflects affective responses to music, while asymmetry entropy reflects autonomic responses to music. Thus, we demonstrate that it is possible to infer activity in the limbic and paralimbic systems from pre-frontal EEG asymmetry. These results show how EEG can be used to measure and monitor changes in the limbic and paralimbic systems. Specifically, they suggest that EEG asymmetry acts as an indicator of sub-cortical changes in activity induced by music. This shows that EEG may be used as a measure of the effectiveness of music therapy to evoke changes in activity in the sub-cortical emotion response network. This is also the first time that the activity of sub-cortical regions, normally considered "invisible" to EEG, has been shown to be characterisable directly from EEG dynamics measured during music listening.
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