On the estimate of music appraisal from surface EEG: a dynamic-network approach based on cross-sensor PAC measurements

Abstract

Objective. The aesthetic evaluation of music is strongly dependent on the listener and reflects manifold brain processes that go well beyond the perception of incident sound. Being a high-level cognitive reaction, it is difficult to predict merely from the acoustic features of the audio signal and this poses serious challenges to contemporary music recommendation systems. We attempted to decode music appraisal from brain activity, recorded via wearable EEG, during music listening. Approach. To comply with the dynamic nature of music stimuli, cross-frequency coupling measurements were employed in a time-evolving manner to capture the evolving interactions between distinct brain-rhythms during music listening. Brain response to music was first represented as a continuous flow of functional couplings referring to both regional and inter-regional brain dynamics and then modelled as an ensemble of time-varying (sub)networks. Dynamic graph centrality measures were derived, next, as the final feature-engineering step and, lastly, a support-vector machine was trained to decode the subjective music appraisal. A carefully designed experimental paradigm provided the labeled brain signals. Main results. Using data from 20 subjects, dynamic programming to tailor the decoder to each subject individually and cross-validation, we demonstrated highly satisfactory performance (MAE= 0.948, R 2= 0.63) that can be attributed, mostly, to interactions of left frontal gamma rhythm. In addition, our music-appraisal decoder was also employed in a part of the DEAP dataset with similar success. Finally, even a generic version of the decoder (common for all subjects) was found to perform sufficiently. Significance. A novel brain signal decoding scheme was introduced and validated empirically on suitable experimental data. It requires simple operations and leaves room for real-time implementation. Both the code and the experimental data are publicly available.

Decoding Listener Identity: Person Identification from High-Frequency EEG Rhythms during Music Perception

“…Dhananjay et al discussed the time and frequency representation of images of EEG signals and classification using convolution neural network (CNN) and recent research discuss the classification of songs based on the inital snippets of brain responses using band power of five frequency bands [7]. Music-EEG is a musical appraisal dataset, collected while participants listening 30 songs and rated their liking scores on 1(Low), 3 (Neutral) and 5 (High) [29]. They computed the cross-frequency coupling with dynamcic graph centrality as features for SVM classifier in different frequency bands.…”

“…Classification of musical brain responses to predict the listened song, the musical appraisal has been rapidly growing because of the intense complexity presented in the musical stimuli. Recent work by Bakas [29] reported the highest performance using gamma rhythms while regressing musical appraisal from EEG responses. Functional networks were constructed from brain responses and the measures corresponding to dynamic graph centrality were extracted.…”

“…Alpha (8 -12 Hz) waves have been found with deep relaxation [20]. Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30 -45 Hz) oscillations are engaged in higher order cognitive functioning [21]. The role of specific brain waves during listener identification may provide insights into brain processes involved in a particular frequency range that account for individual differences.…”

BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition

“…• Common average re-referencing: subtracting the mean of the channels at each timepoint from each channel. • Application of an in-house implementation of w-ICA (independent component analysis) [58] as described in [59].…”

Intensity dependence of sub-harmonics in cortical response to photic stimulation

“…These electrical voltages generated at the human scalp are oscillatory in nature and are termed as brainwaves. Based on their frequency domain, these brainwaves are typically divided into delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma regimes [2,3]. The frequency content of the human brainwaves recorded using EEG has been associated with different cognitive states [3,4].…”