Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

Adamos, Dimitrios A.; Laskaris, Nikolaos A.; Micheloyannis, Sifis

doi:10.1088/1741-2552/aaac36

Cited by 18 publications

(15 citation statements)

References 70 publications

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“…is is actually a process of rhythm detection and beat tracking [5]. In the process of cluster center update, the data objects with the smallest distance to the sample in the cluster were selected as the cluster centers, and then the other data objects were divided into the corresponding clusters by the minimum distance so as to realize the clustering.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

2021

Complexity

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Music rhythm detection and tracking is an important part of the music comprehension system and visualization system. The music signal is subjected to a short-time Fourier transform to obtain the frequency spectrum. According to the perception characteristics of the human auditory system, the spectrum amplitude is logarithmically processed, and the endpoint intensity curve and the phase information of the peak value are output through half-wave rectification. The Pulse Code Modulation (PCM) characteristic value is extracted according to the autocorrelation characteristic of the endpoint intensity curve. This article proposes a rhythm detection algorithm based on multipath search and cluster analysis; that is, based on the clustering algorithm, it absorbs the idea of multipath tracking and proposes its own detection and tracking algorithm. It overcomes the weakness of the clustering algorithm that needs to use Musical Instrument Digital Interface (MIDI) auxiliary input to achieve the desired effect. This algorithm completely uses the PCM signal as the input, which is more robust than the clustering algorithm. The whole process is carried out in the time domain, and the amount of calculation is much smaller than the frequency domain calculation of multipath tracking, and the linear relationship with the rhythm of the music is much better than the filter bank algorithm. This algorithm can successfully detect the rhythm of the music with a strong sense of rhythm and can track the specific position of the rhythm point.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

2021

Complexity

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“…Starting with selectivity, the delineation of a subgraph B of interest, in which the spectral analysis will put emphasis on, is performed using a graph-clustering algorithm in the way that it was recently introduced for sensor-selection in covariance-based decoding of MI [44]. The particular algorithm, known as Dominant-Set (DS) algorithm [49], [50], identifies the most coherent group of nodes in a given connectivity graph. The Appendix provides a short description of its adaptation towards discriminative learning in the setting of multichannel EEG signals.…”

Section: Graph Slepians For Eeg Activity From Cortical Networkmentioning

confidence: 99%

Covariation Informed Graph Slepians for Motor Imagery Decoding

Georgiadis

Adamos

Nikolopoulos

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Graph signal processing (GSP) provides signal analytic tools for data defined in irregular domains, as is the case of non-invasive electroencephalography (EEG). In this work, the recently introduced technique of Graph Slepian functions is exploited for the robust decoding of motor imagery (MI) brain activity. The particular technique builds over the concept of graph Fourier transform (GFT) and provides additional flexibility in the subsequent data analysis by incorporating domain knowledge. Based on contrastive learning, we introduce an algorithmic pipeline that attains a data driven and subject specific design of Graph Slepian functions. These functions, by incorporating both the topology of the sensor array and the empirical evidence about the differential functional covariation, act as spatial filters that enhance the information conveyed by the multichannel signal and specifically relates to the participant's intention. The proposed technique for crafting Graph Slepians is incorporated in a MI-decoding scheme, in which the informed projections are fed to a support vector machine (SVM) that casts a prediction regarding the type of intended movement. The employed MI-decoder is evaluated based on two publicly available datasets and its superiority against popular alternatives in the field is established. Computational efficiency is listed among its main advantages, since it involves only simple matrix operations, allowing to consider its use in real-time implementations.

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“…However studies have shown that simply listening to or tapping along with the rhythmic structure of music entrains the brain's low-frequency oscillations (Lakatos et al, 2008;Besle et al, 2011). For example, passive listening to musical sequences induces changes within alpha (Bridwell et al, 2017) and increases coupling between delta and high beta frequency ranges (Adamos et al, 2018). Word lists that are sung rather than spoken increase alpha coherence in bilateral frontal areas (Thaut et al, 2005) known to support learning-related processes (Sato et al, 2018).…”

Section: Entrainment Of Neural Oscillationsmentioning

confidence: 99%

Music Training, Working Memory, and Neural Oscillations: A Review

et al. 2020

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This review focuses on reports that link music training to working memory and neural oscillations. Music training is increasingly associated with improvement in working memory, which is strongly related to both localized and distributed patterns of neural oscillations. Importantly, there is a small but growing number of reports of relationships between music training, working memory, and neural oscillations in adults. Taken together, these studies make important contributions to our understanding of the neural mechanisms that support effects of music training on behavioral measures of executive functions. In addition, they reveal gaps in our knowledge that hold promise for further investigation. The current review is divided into the main sections that follow: (1) discussion of behavioral measures of working memory, and effects of music training on working memory in adults; (2) relationships between music training and neural oscillations during temporal stages of working memory; (3) relationships between music training and working memory in children; (4) relationships between music training and working memory in older adults; and (5) effects of entrainment of neural oscillations on cognitive processing. We conclude that the study of neural oscillations is proving useful in elucidating the neural mechanisms of relationships between music training and the temporal stages of working memory. Moreover, a lifespan approach to these studies will likely reveal strategies to improve and maintain executive function during development and aging.

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Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

Cited by 18 publications

References 70 publications

[Retracted] Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

[Retracted] Music Rhythm Detection Algorithm Based on Multipath Search and Cluster Analysis

Covariation Informed Graph Slepians for Motor Imagery Decoding

Music Training, Working Memory, and Neural Oscillations: A Review

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