Revealing Preference in Popular Music Through Familiarity and Brain Response

Sangnark, Soravitt; Autthasan, Phairot; Ponglertnapakorn, Puntawat; Chalekarn, Phudit; Sudhawiyangkul, Thapanun; Trakulruangroj, Manatsanan; Songsermsawad, Sarita; Assabumrungrat, Rawin; Amplod, Supalak; Ounjai, Kajornvut; Wilaiprasitporn, Theerawit

doi:10.1109/jsen.2021.3073040

“…Privacy preservation: a note for sleep monitoring Brain waves are a rich source of information from which deciphering numerous sensitive pieces of information has been shown possible, such as identity(Palaniappan and Mandic 2007), age (Al Zoubi et al 2018), gender(Wang and Hu 2019), emotion (Alarcao and Fonseca 2019), preference(Sangnark et al 2021), personality(Zhao et al 2018), etc. This poses a challenge to protect this information from the user's perspective and to comply with legal restrictions, such as the General Data Protection Regulation (2021) in the European Union and the Consumer Privacy Bill of Rights(Gaff et al 2014) in the US.…”

mentioning

confidence: 99%

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Phan

¹

,

Mikkelsen

²

2022

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Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

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“…In another aspect, The choice of music was relatively personalized and varied considerably in different studies. An endeavor (Sangnark et al, 2021 ) have been made toward the perceptual distinctions from listening to two music types: music without lyrics (Melody) and music with lyrics (Song), which denoted that music of different forms could bring different emotional information within. Increasing numbers of researchers have been willing to using standard music databases for study as MER continues to develop, which eliminates the subjective operation of music selection and reduces the inconsistency of outcomes of different studies.…”

Section: Eeg-based Music Emotion Recognitionmentioning

confidence: 99%

A review: Music-emotion recognition and analysis based on EEG signals

Cui

¹

,

Wu

²

,

Wu

³

et al. 2022

Front. Neuroinform.

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Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

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“…Brain waves are a rich source of information from which deciphering numerous sensitive piece of information has been shown possible, such as identity [95], age [101], gender [102], emotion [103], preference [104], personality [105], etc. This poses a challenge to protect this information from user perspective and to comply with legal restrictions, such as General Data Protection Regulation (GDPR) [106] in European Union and Consumer Privacy Bill of Rights (CPBR) [107] in the US.…”

Section: Privacy Preservation: a Note For Sleep Monitoringmentioning

confidence: 99%

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

Phan,

Mikkelsen

2021

Preprint

0

View full text Add to dashboard Cite

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

show abstract

Revealing Preference in Popular Music Through Familiarity and Brain Response

Cited by 18 publications

References 43 publications

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

A review: Music-emotion recognition and analysis based on EEG signals

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

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