Brain-Computer Music Interface, a bibliometric analysis

Torres-Cardona, Héctor Fabio; Aguirre-Grisales, Catalina

doi:10.1080/2326263x.2022.2109313

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The first attempt to connect the brain to music was made by Adrian and Matthew as they found a correlation between Posterior Dominant Rhythm (PDR) and reproduced brain signals played over a loudspeaker [ 3 ]; this was followed by the creation of the first brain-controlled percussion instrument in 1965 that worked based on PDR signals [ 4 , 5 ]. Similar to the case of BCI, the BCMI applications are advantageous in various fields spanning the entertainment industry and healthcare, such as in music therapy.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application

Ghodousi

Pousson

Bernhofs

et al. 2023

Sensors

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A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.

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Section: Introductionmentioning

confidence: 99%

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application

Ghodousi

Pousson

Bernhofs

et al. 2023

Sensors

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Brain-Computer Music Interface (BCMI): Exploring Creative Expression

Xu,

Yin

2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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A Bibliometric Review of Brain–Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics

Chio,

Quiles-Cucarella

2024

Sensors

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In this paper, a bibliometric review is conducted on brain–computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain–computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.

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Brain-Computer Music Interface, a bibliometric analysis

Cited by 3 publications

References 20 publications

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application

Brain-Computer Music Interface (BCMI): Exploring Creative Expression

A Bibliometric Review of Brain–Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics

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