Author

Tapani Ristaniemi

University of JyväskyläORCID 0000-0002-8461-3968Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Approach:statistical model· 3 Research Focus:clustering· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Fengyu CongiD· 2 shared Lauri T. ParkkoneniD· 2 shared Petri ToiviaineniD· 2 shared Tiina M. ParviaineniD· 2 shared Aapo HyvärineniD· 1 shared Anja ThiedeiD· 1 shared Asoke Kumar NandiiD· 1 shared Brigitte Bogert· 1 shared

Activity by Year

2019-2020 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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2 shared papersNeuroscience

Fengyu CongORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Guanghui Zhang, Tapani Ristaniemi, Fengyu Cong

This paper introduces a data-driven method to objectively extract evoked event-related oscillations from ERP data, showing improved detection compared to conventional methods.

Neural Plasticity · Dec 19, 2020Peer-reviewedNeuroscience

Musicianship can be decoded from magnetic resonance images

Tuomas Puoliväli, Tuomo Sipola, Anja Thiede, Marina Kliuchko, Brigitte Bogert, Petri Toiviainen, Asoke Kumar Nandi, Lauri T. Parkkonen, Elvira Brattico, Tapani Ristaniemi, Tiina M. Parviainen

The paper shows that musicianship can be predicted from brain structure MRI data using machine learning, achieving significant classification accuracy.

bioRxiv · Jul 19, 2020 · PreprintNeuroscience

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Yongjie Zhu, Chi Zhang, Hanna Poikonen, Petri Toiviainen, Minna Huotilainen, Klaus Mathiak, Tapani Ristaniemi, Fengyu Cong

Brain Topography · Mar 2, 2020Peer-reviewedNeuroscience

PCA-based source-space contrast maps reveal psychologically meaningful individual differences in continuous MEG activity

Erkka Heinilä, Aapo Hyvärinen, Tapani Ristaniemi, Lauri T. Parkkonen, Tiina M. Parviainen

The paper uses PCA on MEG data to identify brain activity patterns linked to psychological traits during focused attention and mind wandering.

bioRxiv · Nov 9, 2019 · PreprintNeuroscience