Brain-Voyant: A General Purpose Machine-Learning Tool for Real-Time fMRI Whole-Brain Pattern Classification

Ori Cohen, Rafael Malach, Moshe Koppel, Doron Friedman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We have developed Brain-Voyant, an efficient general-purpose machine learning tool for real-time functional magnetic resonance imaging classification using whole-brain data, which can be used to explore novel brain-computer interface paradigms or advanced neurofeedback protocols. We have created a convenient and configurable front-end tool that receives fMRI-based multi-voxel raw brain data as input. Our tool processes, analyses, classifies and transfers the classification to an external object such as a virtual avatar or a humanoid robot in real-time. Our tool is focused on minimizing delay time, and to that end, it employs a method that is based on examining in advance the voxels that have been found to be task-relevant in the machine learning model training phase.The tool's code base was designed to be easily extended to support additional feature reduction, normalization and classification algorithms. This tool was used in several published studies using motor execution, motor imagery, and visual category classification in cue-based and free-choice brain-computer interface experiments, with both healthy and amputated subjects. This tool is not limited by number of classes, is not limited to predefined regions of interest, and classifier instances can run in parallel to combine multiple classification tasks in real time. Finally, our tool is able use the slow peaking blood-oxygen-level dependent signal to classify our subjects' intention during the two-second window TR. We release this tool as open-source for non-commercial usage.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
StatePublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

VII. ACKNOWLEDGEMENTS This research is supported by the European Union FP7 Integrated Project VERE (No 657295), www.vereproject.eu. We would like to thank the Weizmann Institute fMRI scanner staff Edna Furman-Haran, Nachum Stern and Fanny Attar for their help and the subjects for their participation.

FundersFunder number
European Union FP7
VERE657295

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