Author

Madoka Nakajima

Juntendo UniversityORCID 0000-0002-8496-2973Unclaimed 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· 2 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Hidenori SuganoiD· 2 shared Haruhiko KishimaiD· 1 shared Hiroharu SuzukiiD· 1 shared Hui Ming KhooiD· 1 shared Huixiang YangiD· 1 shared Kentaro Tamura· 1 shared Kosuke FukumoriiD· 1 shared Naoki TaniiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersNeuroscience

Hidenori SuganoORCID

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2 papers

Recent·Most cited

Image retrieval based on closed-loop visual-semantic neural decoding

Ryohei Fukuma, Takufumi Yanagisawa, Hidenori Sugano, Kentaro Tamura, Satoru Oshino, Naoki Tani, Yasushi Iimura, Hui Ming Khoo, Hiroharu Suzuki, Huixiang Yang, Takamitsu Iwata, Madoka Nakajima, Shinji Nishimoto, Yukiyasu Kamitani, Haruhiko Kishima

This paper demonstrates that closed-loop brain-computer interfaces can retrieve intended images using a CLIP latent space, achieving over 80% accuracy, while failing with AlexNet.

bioRxiv · Aug 5, 2024 · PreprintNeuroscience

Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

Kosuke Fukumori, Noboru Yoshida, Hidenori Sugano, Madoka Nakajima, Toshihisa Tanaka

This paper introduces data-driven neural networks with linear-phase filters for detecting epileptic spikes in EEG data, achieving an average AUC of 0.967.

IEEE Journal of Biomedical and Health Informatics · Aug 6, 2021Peer-reviewedNeuroscience