Manuscripts on this dataset

1. 

   CMS Manager @ 10:54 am on 01 Feb 2024

   Manuscripts on this dataset, "ds003774"

2. 

   CMS Manager @ 6:03 pm on 12 Feb 2024

   1. P Pandey, PS Bedmutha, Stronger correlation of music features with brain signals predicts increased levels of enjoyment, 2023 IEEE Applied Sensing Conference (APSCON), 2023, Cited by 2, https://ieeexplore.ieee.org/abstract/document/10101229/
   2. P Pandey, KP Miyapuram, D Lomas, Non-linear features of β brain rhythms predict listener-specific neural signature in naturalistic music listening, Authorea Preprints, 2023, Cited by 2, https://www.techrxiv.org/doi/full/10.36227/techrxiv.20502423.v1
   3. KP Miyapuram, N Ahmad, P Pandey, JD Lomas, Electroencephalography (EEG) dataset during naturalistic music listening comprising different genres with familiarity and enjoyment ratings, Data in Brief, 2022, Cited by 3, https://www.sciencedirect.com/science/article/pii/S235234092200868X
   4. P Pandey, N Ahmad, KP Miyapuram, Predicting dominant beat frequency from brain responses while listening to music, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, Cited by 11, https://ieeexplore.ieee.org/abstract/document/9669750/
   5. KP Miyapuram, ID Lomas, Canonical Correlation Analysis (CCA) Reveal Neural Entrainment For Each Song And Similarity Among Genres, Cited by 0, https://www.researchgate.net/profile/Pankaj-Pandey-12/publication/362537879_Canonical_Correlation_Analysis_Reveal_Neural_Entrainment_for_Each_Song_and_Similarity_among_Genres/links/62ef870f505511283e9900ad/Canonical-Correlation-Analysis-Reveal-Neural-Entrainment-for-Each-Song-and-Similarity-among-Genres.pdf
   6. KP Miyapuram, P Pandey, N Ahmad, BR Shiraguppi, Music listening-genre EEG dataset (MUSIN-G), Cited by 6

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