Decoding visual perception from human brain activity

Abstract

Event Abstract Back to Event Decoding visual perception from human brain activity Yukiyasu Kamitani1* 1 ATR (Advanced Telecommunications Research Institute International), Japan Objective assessment of mental experience in terms of brain activity represents a major challenge in neuroscience. Despite its wide-spread use in human brain mapping, functional magnetic resonance imaging (fMRI) has been thought to lack the resolution to probe into putative neural representations of perceptual and behavioral features, which are often found in neural clusters smaller than the size of single fMRI voxels. As a consequence, the potential for reading out mental contents from human brain activity, or ‘neural decoding’, has not been fully explored. In this talk, I present our recent work on the decoding of fMRI signals based on machine learning-based analysis. I first show that visual features represented in ‘subvoxel’ neural structures can be decoded from ensemble fMRI responses, using a machine learning model (‘decoder’) trained on sample fMRI responses to visual features. Decoding of stimulus features is extended to the method for ‘neural mind-reading’, which predicts a person's subjective state using a decoder trained with unambiguous stimulus presentation. Various applications of this approach will be presented including fMRI-based brain-machine interface. We next discuss how a multivoxel pattern can represent more information than the sum of individual voxels, and how an effective set of voxels for decoding can be selected from all available ones. Finally, a modular decoding approach is presented in which a wide variety of contents can be predicted by combining the outputs of multiple modular decoders. I demonstrate an example of visual image reconstruction where binary 10 x 10-pixel images (2^00 possible states) can be accurately reconstructed from a singe-trial or single-volume fMRI signals, using a small number of training data. Our approach thus provides an effective means to read out complex mental states from brain activity while discovering information representation in multi-voxel patterns. Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010. Presentation Type: Oral Presentation Topic: Workshop 3: Neuroinformatics of BMI: decoding and control of neural codes Citation: Kamitani Y (2010). Decoding visual perception from human brain activity. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00007 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Jun 2010; Published Online: 08 Jun 2010. * Correspondence: Yukiyasu Kamitani, ATR (Advanced Telecommunications Research Institute International), Kyoto, Japan, kamitani@i.kyoto-u.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yukiyasu Kamitani Google Yukiyasu Kamitani Google Scholar Yukiyasu Kamitani PubMed Yukiyasu Kamitani Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.