Neuropsychiatric symptoms (NPS) in dementia can be reduced through music listening. Little has been reported on home-based listening, compilation processes, or individual responses that include biophysiological data. We aim to provide new insights from two home-based case studies focused on specific music selections.
View Article and Find Full Text PDFMusic provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFFront Hum Neurosci
October 2017
Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute).
View Article and Find Full Text PDFObjective: We aim to develop and evaluate an affective brain-computer music interface (aBCMI) for modulating the affective states of its users.
Approach: An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions.
It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
October 2015
The neural mechanisms of music listening and appreciation are not yet completely understood. Based on the apparent relationship between the beats per minute (tempo) of music and the desire to move (for example feet tapping) induced while listening to that music it is hypothesised that musical tempo may evoke movement related activity in the brain. Participants are instructed to listen, without moving, to a large range of musical pieces spanning a range of styles and tempos during an electroencephalogram (EEG) experiment.
View Article and Find Full Text PDFJ Neurosci Methods
March 2015
Background: The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.
New Method: A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.
This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses. We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex.
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