Related papers: An EEG study of creativity in expert classical mus…
In this paper, we present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each…
Creativity is a core cognitive capacity underlying innovation and adaptive problem solving, yet how it is represented in the brain's intrinsic functional architecture is not fully understood. While resting-state fMRI studies have identified…
The subject of this work is to check how different types of music affect human emotions. While listening to music, a subjective survey and brain activity measurements were carried out using an EEG helmet. The aim is to demonstrate the…
This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of…
Music large-scale form, the structure of musical units ranging over several bars, are studied using EEG measurements of 25 participants listened to the first four minutes of a piece of electronic dance music (EDM). Grand-averages of…
What happens inside the performers brain when he is performing and composing a particular raga. Are there some specific regions in brain which are activated when an artist is creating or imaging a raga in his brain. Do the regions remain…
The aim of this study is to investigate bursts- related EEG signals in a focal hand dystonia patient. Despite of considering time domain and frequency domain techniques as mutually exclusive analysis, in this contribution we have taken…
Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process…
Auditory processing difficulties involve challenges in understanding speech in noisy environments despite normal hearing. However, the neural mechanisms remain unclear, and standardized diagnostic criteria are lacking. This study examined…
Nowadays, personalized recommender systems play an increasingly important role in music scenarios in our daily life with the preference prediction ability. However, existing methods mainly rely on users' implicit feedback (e.g., click,…
Self-tracking has been long discussed, which can monitor daily activities and help users to recall previous experiences. Such data-capturing technique is no longer limited to photos, text messages, or personal diaries in recent years. With…
Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals…
Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the…
The extent of intra-individual and inter-individual variability is an important factor in determining the statistical, and hence possibly clinical, significance of observed differences in the EEG. This study investigates the changes in…
Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to…
A growing interest has developed in the problem of training models of EEG features to predict brain activity measured using fMRI, i.e. the problem of EEG-to-fMRI synthesis. Despite some reported success, the statistical significance and…
This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as repertoire, diatonic modal etudes, and…
Electroencephalogram (EEG) data is crucial for diagnosing mental health conditions but is costly and time-consuming to collect at scale. Synthetic data generation offers a promising solution to augment datasets for machine learning…
Advances in wireless electroencephalography (EEG) technology promise to record brain-electrical activity in everyday situations. To better understand the relationship between brain activity and natural behavior, it is necessary to monitor…
Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental…