Seung-Goo Kim

Seung-Goo [IPA:sɯŋgu] KIM (김승구;金勝九) is a cognitive neuroscientist specialized in human neuroimaging and auditory processing. Seung-Goo uses computational models of sounds and non-invasive human neuroimaging techniques (functional/structural MRI and M/EEG source imaging) to investigate (1) how the musical sounds are represented in the human brain and (2) how these neural representations are transformed to evoke intense emotions.

After a Ph.D. program at Max Planck Institute for Human Cognitive and Brain Sciences, Germany in 2017 (PI: Dr. Thomas R. Knösche), he has worked as a postdoc at Duke University (PI: Dr. Tobias Overath), and currently works as a research scientist at Max Planck Institute for Empirical Aesthetics (PI: Dr. Daniela Sammler).

news

Feb 23, 2026	Spearheaded by Dr. Sarah Faber, our symposium proposal has been accepted for the 2026 OHBM Annual Meeting! The symposium will be held on June 2026, and the title is “Sound and Music: Naturalistic Approaches to Auditory–Motor and Affective Brain Dynamics”. We are looking forward to meeting you in Bordeaux, France! 🇫🇷🍷
Feb 16, 2026	To initiate an international collaboration between MPIEA (🇩🇪) and KAIST (🇰🇷), our bilateral workshop proposal on “Affective Voice Synthesis: Industrial and Scientific Perspectives on Technology” has been accepted by the German Research Foundation (DFG) and the National Research Foundation of Korea (NRF)!
Nov 06, 2025	ManyMusic stimuli sets are published! See our new paper!
Jul 16, 2025	Do you live in Frankfurt am Main area and love music❤️ 🎶?
Jul 12, 2025	I am attending ICMPC18 in São Paulo, Brazil from July 21 to 25, 2025. I will present: (1) a workshop on LEA (July 21, 16:45-18:15, Bd 45, Rm 501), (2) a spoken presentation on ManyMusic-stimuli (July 25, Se44-Emotion, Rm 502). See you in Brazil! 🇧🇷

latest posts

Mar 23, 2025	Surfing LEA 🏄‍♀️👧
Mar 13, 2025	TeaP2025 ended
Sep 15, 2024	ChatGPT4o for BASH one-liners

selected publications

ManyMusic: An Open-access Music Audio Dataset for Human Experiments on Musical Emotions

Seung-Goo Kim, Pablo Alonso-Jiménez, Till Bechtloff, and 2 more authors

Proceedings – 3rd Workshop on Human-Centric Music Information Research (HCMIR25), Daejeon, Republic of Korea, 20 September 2025, Sep 2025

DOI Bib PDF

@article{kim2025mms,
  author = {Kim, Seung-Goo and Alonso-Jiménez, Pablo and Bechtloff, Till and Sammler, Daniela and Bogdanov, Dmitry},
  title = {ManyMusic: An Open-access Music Audio Dataset for Human Experiments on Musical Emotions},
  journal = {Proceedings -- 3rd Workshop on Human-Centric Music Information Research (HCMIR25), Daejeon, Republic of Korea, 20 September 2025},
  month = sep,
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.17346525},
  url = {https://doi.org/10.5281/zenodo.17346525},
}

preprint
Reverse Double-Dipping: When Data Dips You, Twice—Stimulus-Driven Information Leakage in Naturalistic Neuroimaging

Seung-Goo Kim

bioRxiv, Sep 2025

Abs DOI Bib HTML PDF

This article elucidates a methodological pitfall of cross-validation for evaluating predictive models applied to naturalistic neuroimaging data–namely, ’reverse double-dipping’ (RDD). In a broader context, this problem is also known as ’leakage in training examples’, which poses challenges in detecting it in practice. This issue can occur when predictive modeling is employed with data from a conventional neuroscientific design, characterized by a limited set of stimuli repeated across trials and/or participants, resulting in spurious predictive performances due to overfitting to repeated signals, even in the presence of independent noise. Through comprehensive simulations and real-world examples following theoretical formulation, the article underscores how such information leakage can occur and how severely it could compromise the analysis when it is combined with widely spread informal reverse inference. The article concludes with practical recommendations for researchers to avoid RDD in their experiment design and analysis.Competing Interest StatementThe authors have declared no competing interest.
@article{kim2025rdd, author = {Kim, Seung-Goo}, doi = {10.1101/2025.04.01.646146}, elocation-id = {2025.04.01.646146}, eprint = {https://www.biorxiv.org/content/early/2025/04/05/2025.04.01.646146.full.pdf}, journal = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {Reverse Double-Dipping: When Data Dips You, Twice{\textemdash}Stimulus-Driven Information Leakage in Naturalistic Neuroimaging}, year = {2025}, bdsk-url-1 = {https://www.biorxiv.org/content/early/2025/04/05/2025.04.01.646146}, bdsk-url-2 = {https://doi.org/10.1101/2025.04.01.646146}, }

CerCor

Linguistic modulation of the neural encoding of phonemes

Seung-Goo Kim, Federico De Martino, and Tobias Overath

Cerebral Cortex, Sep 2024

DOI Bib HTML PDF Code

@article{kim2024cc,
  journal = {Cerebral Cortex},
  author = {Kim, Seung-Goo and Martino, Federico De and Overath, Tobias},
  date-modified = {2024-04-25 14:43:05 +0200},
  doi = {10.1093/cercor/bhae155},
  title = {Linguistic modulation of the neural encoding of phonemes},
  year = {2024},
}

MEG correlates of temporal regularity relevant to pitch perception in human auditory cortex

Seung-Goo Kim^*, Tobias Overath^*, William Sedley, and 5 more authors

NeuroImage, Apr 2022

DOI Bib HTML PDF Code

@article{kim2022ni,
  author = {Kim, Seung-Goo and Overath, Tobias and Sedley, William and Kumar, Sukhbinder and Teki, Sundeep and Kikuchi, Yukiko and Patterson, Roy and Griffiths, Timothy D.},
  doi = {10.1016/j.neuroimage.2022.118879},
  journal = {{NeuroImage}},
  month = apr,
  pages = {118879},
  publisher = {Elsevier {BV}},
  title = {{MEG} correlates of temporal regularity relevant to pitch perception in human auditory cortex},
  volume = {249},
  year = {2022},
}

FNsci
On the encoding of natural music in computational models and human brains

Seung-Goo Kim

Frontiers in Neuroscience, Apr 2022

Abs DOI Bib HTML PDF

This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music.
@article{kim2022fn, author = {Kim, Seung-Goo}, doi = {10.3389/fnins.2022.928841}, issn = {1662-453X}, journal = {Frontiers in Neuroscience}, title = {On the encoding of natural music in computational models and human brains}, volume = {16}, year = {2022}, bdsk-url-1 = {https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.928841}, bdsk-url-2 = {https://doi.org/10.3389/fnins.2022.928841} }