Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.Peer reviewe

MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music

International audienceWe present MAD-EEG, a new, freely available dataset for studying EEG-based auditory attention decoding considering the challenging case of subjects attending to a target instrument in polyphonic music. The dataset represents the first music-related EEG dataset of its kind, enabling, in particular, studies on single-trial EEG-based attention decoding, while also opening the path for research on other EEG-based music analysis tasks. MAD-EEG has so far collected 20-channel EEG signals recorded from 8 subjects listening to solo, duo and trio music excerpts and attending to one pre-specified instrument. The proposed experimental setting differs from the ones previously considered as the stimuli are polyphonic and are played to the subject using speakers instead of headphones. The stimuli were designed considering variations in terms of number and type of instruments in the mixture, spatial rendering, music genre and melody that is played. Preliminary results obtained with a state-of-the-art stimulus reconstruction algorithm commonly used for speech stimuli show that the audio representation reconstructed from the EEG response is more correlated with that of the attended source than with the one of the unattended source, proving the dataset to be suitable for such kind of studies

Error Signals from the Brain: 7th Mismatch Negativity Conference

The 7th Mismatch Negativity Conference presents the state of the art in methods, theory, and application (basic and clinical research) of the MMN (and related error signals of the brain). Moreover, there will be two pre-conference workshops: one on the design of MMN studies and the analysis and interpretation of MMN data, and one on the visual MMN (with 20 presentations). There will be more than 40 presentations on hot topics of MMN grouped into thirteen symposia, and about 130 poster presentations. Keynote lectures by Kimmo Alho, Angela D. Friederici, and Israel Nelken will round off the program by covering topics related to and beyond MMN

On the encoding of natural music in computational models and human brains

This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective