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

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.