Name: Kamitani Lab
Type: Organization
Bio: Sharing code and data from Kamitani Lab (PI: Yukiyasu Kamitani) at Kyoto University and ATR: brain decoding, neuroimaging, machine learning, neuroinformatics
Location: Kyoto, Japan
Blog: http://kamitani-lab.ist.i.kyoto-u.ac.jp/
Kamitani Lab's Projects
Python package for brain decoding analysis (BrainDecoderToolbox2 data format, machine learning analysis, functional MRI)
Python package for brain decoding analysis (BrainDecoderToolbox2 data format, machine learning analysis, functional MRI)
Python API for datasets published from Kamitani Lab, Kyoto Univ and ATR.
Matlab library for brain decoding analysis (BrainDecoderToolbox2 data format, machine learning analysis, functional MRI)
An open-source program to facilitate time-alignment of neurophysiological data. This is being managed by ATR in Kyoto, Japan (www.atr.jp).
Open source conversion libraries for the BrainLiner HDF5 data format.
Documentation and examples about the BrainLiner HDF5 data format.
Data and code for Shen, Horikawa, Majima, and Kamitani (2019) Deep image reconstruction from human brain activity. PLoS Comput. Biol. http://dx.doi.org/10.1371/journal.pcbi.1006633.
Data and code for reproducing results of Horikawa, Cowen, Keltner, and Kamitani (2020) The neural representation of visually evoked emotion is high-dimensional, categorical, and distributed across transmodal brain regions. iScience (https://www.cell.com/iscience/fulltext/S2589-0042(20)30245-5).
Functional localizer experiment used to define category-selective cortical regions
fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Demo code for Horikawa and Kamitani (2017) Generic decoding of seen and imagined objects using hierarchical visual features. Nat Commun https://www.nature.com/articles/ncomms15037.
To share captions of stimuli dataset
Codes used in "Neural decoding of visual imagery during sleep" by Horikawa et al (Science, 2013, http://science.sciencemag.org/content/340/6132/639.long)
iCNN: image reconstruction from CNN features
A reconstruction framework for materializing subjective experiences from brain signals
Matlab functions to generate pattern images (sine grating, concentric sine, Gabor patch, and random dots) and draw basic figures (filled/frame oval/rectangle, line, and polygon)