Music Familiarity Affects EEG Entrainment When Little Attention Is Paid

Alpha band modulation caused by selective attention to music enables EEG classification

Humans are able to pay selective attention to music or speech in the presence of multiple sounds. It has been reported that in the speech domain, selective attention enhances the cross-correlation between the envelope of speech and electroencephalogram (EEG) while also affecting the spatial modulation of the alpha band. However, when multiple music pieces are performed at the same time, it is unclear how selective attention affects neural entrainment and spatial modulation. In this paper, we hypothesized that the entrainment to the attended music differs from that to the unattended music and that spatial modulation in the alpha band occurs in conjunction with attention. We conducted experiments in which we presented musical excerpts to 15 participants, each listening to two excerpts simultaneously but paying attention to one of the two. The results showed that the cross-correlation function between the EEG signal and the envelope of the unattended melody had a more prominent peak than that of the attended melody, contrary to the findings for speech. In addition, the spatial modulation in the alpha band was found with a data-driven approach called the common spatial pattern method. Classification of the EEG signal with a support vector machine identified attended melodies and achieved an accuracy of 100% for 11 of the 15 participants. These results suggest that selective attention to music suppresses entrainment to the melody and that spatial modulation of the alpha band occurs in conjunction with attention. To the best of our knowledge, this is the first report to detect attended music consisting of several types of music notes only with EEG.

The effect of familiarity on neural tracking of music stimuli is modulated by mind wandering

One way to investigate the cortical tracking of continuous auditory stimuli is to use the stimulus reconstruction approach. However, the cognitive and behavioral factors impacting this cortical representation remain largely overlooked. Two possible candidates are familiarity with the stimulus and the ability to resist internal distractions. To explore the possible impacts of these two factors on the cortical representation of natural music stimuli, forty-one participants listened to monodic natural music stimuli while we recorded their neural activity. Using the stimulus reconstruction approach and linear mixed models, we found that familiarity positively impacted the reconstruction accuracy of music stimuli and that this effect of familiarity was modulated by mind wandering.

Examining the capability for rhythmic synchronization and music production in vocal learning parrot species

Vocal production learning and beat perception and synchronization (BPS) share some common characteristics, which makes the vocal learning and rhythmic synchronization hypothesis (VLH) a reasonable explanation for the evolution of the capability for rhythmic synchronization. However, even in vocal learners, it is rare to see non-human animals demonstrate BPS to human music. Therefore, the first objective of this article is to propose some possible reasons why we do not see BPS in budgerigars, an excellent vocal learning species, while presenting some of my own findings. The second objective of this article is to propose a seamless bridge to connect the capability for vocal learning and BPS in locomotion. For this purpose, I present my own findings, wherein cockatiels spontaneously sang in synchrony with a melody of human music. This behavior can be considered a vocal version of BPS. Therefore, it can establish a connection between these two capabilities. This article agrees with the possibility that some mechanisms other than the vocal learning system may enable BPS, contrary to the original idea of VLH. Nevertheless, it is still reasonable to connect the capability for vocal learning and that for BPS. At the very least, the capability for vocal learning may contribute to the evolution of BPS. From these arguments, this article also proposes a scenario which includes vocalizing in synchrony as a driving force for the evolution of BPS and the capability for music production.

Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience

Neural activity in the auditory system synchronizes to sound rhythms, and brain–environment synchronization is thought to be fundamental to successful auditory perception. Sound rhythms are often operationalized in terms of the sound’s amplitude envelope. We hypothesized that – especially for music – the envelope might not best capture the complex spectro-temporal fluctuations that give rise to beat perception and synchronized neural activity. This study investigated (1) neural synchronization to different musical features, (2) tempo-dependence of neural synchronization, and (3) dependence of synchronization on familiarity, enjoyment, and ease of beat perception. In this electroencephalography study, 37 human participants listened to tempo-modulated music (1–4 Hz). Independent of whether the analysis approach was based on temporal response functions (TRFs) or reliable components analysis (RCA), the spectral flux of music – as opposed to the amplitude envelope – evoked strongest neural synchronization. Moreover, music with slower beat rates, high familiarity, and easy-to-perceive beats elicited the strongest neural response. Our results demonstrate the importance of spectro-temporal fluctuations in music for driving neural synchronization, and highlight its sensitivity to musical tempo, familiarity, and beat salience.

When we listen to a melody, the activity of our neurons synchronizes to the music: in fact, it is likely that the closer the match, the better we can perceive the piece. However, it remains unclear exactly which musical features our brain cells synchronize to. Previous studies, which have often used ‘simplified’ music, have highlighted that the amplitude envelope (how the intensity of the sounds changes over time) could be involved in this phenomenon, alongside factors such as musical training, attention, familiarity with the piece or even enjoyment. Whether differences in neural synchronization could explain why musical tastes vary between people is also still a matter of debate.

In their study, Weineck et al. aim to better understand what drives neuronal synchronization to music. A technique known as electroencephalography was used to record brain activity in 37 volunteers listening to instrumental music whose tempo ranged from 60 to 240 beats per minute. The tunes varied across an array of features such as familiarity, enjoyment and how easy the beat was to perceive. Two different approaches were then used to calculate neural synchronization, which yielded converging results.

The analyses revealed that three types of factors were associated with a strong neural synchronization. First, amongst the various cadences, a tempo of 60-120 beats per minute elicited the strongest match with neuronal activity. Interestingly, this beat is commonly found in Western pop music, is usually preferred by listeners, and often matches spontaneous body rhythms such as walking pace. Second, synchronization was linked to variations in pitch and sound quality (known as ‘spectral flux’) rather than in the amplitude envelope. And finally, familiarity and perceived beat saliency – but not enjoyment or musical expertise – were connected to stronger synchronization.

These findings help to better understand how our brains allow us to perceive and connect with music. The work conducted by Weineck et al. should help other researchers to investigate this field; in particular, it shows how important it is to consider spectral flux rather than amplitude envelope in experiments that use actual music.

Intelligent Analysis and Classification of Piano Music Gestures with Multimodal Recordings

In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing K-Means algorithm and achieved an accuracy of 99.12%.

Resonance as a Design Strategy for AI and Social Robots

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human-robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of "sympathetic resonance" as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human-robot interactions.