Brain Innovation

Software Made with Passion

Screenshot showing TBV User Interface on Windows. Here the multi-slice (matrix) view has been selected in the toolbar at the bottom left side.

Click on this and other images on this page to see enlarged versions.

Turbo-BrainVoyager

Turbo-BrainVoyager (TBV) is a highly optimized, easy-to- use software package for the real-time analysis and dynamic visualization of functional magnetic resonance imaging data sets. Turbo-BrainVoyager allows to observe the working brain "online" by incrementally computing statistical maps as contrasts of a General Linear Model (GLM). The program also performs real-time pre-rocessing to improve data quality, including 3D motion correction, spatial Gaussian smoothing and temporal high-pass filtering (drift removal). TBV visualizes the data in various formats including a multi-slice (matrix) view, a single (zoomed) slice view and a anatomical volume view. The data can also be visualized on 3D anatomical data sets in AC-PC, Talairach and MNI space enabling import and export of regions and masks in normalized space. Furthermore, data can be visualized on rendered cortex meshes that can be rotated and zoomed. Statistical results are shown as real-time activation movies on all views. Besides standard real-time tasks, such as quality assurance, the powerful computational and visualization capabilities of the proram allow advanced applications, such as neurofeedback, communication BCIs and hyperscanning.

Basic Operations

Performing real-time analysis with Turbo-BrainVoyager requires specification of a TBV JSON settings file containing information about the upcoming functional measurements. If a proper TBV settings file has been prepared - usually with the help of the TBV Settings dialog - real-time analysis can be started by clicking the red Start button.
To observe time courses, regions-of-Interest (ROIs) can be loaded or easily defined interactively in any of the provided visualizations. The time courses as well as event-related averaging plots are shown immediately for any of the defined ROIs and are updated as new data becomes available.
In quality assurance applications, head motion correction and the inspection of statistical maps and time courses help to decide whether an ongoing fMRI measurement works as expected. If problems are detected, the scan can be repeated immediately while the subject or patient is still in the scanner. Besides analyzing single functional runs, the program also allows GLM analyses across functional (e.g., localizer) runs if all runs are aligned to the same volume. Previously recorded runs can be quickly reloaded and inspected at any time.

Screenshot showing TBV User Interface on macOS. Here the anatomical view has been selected in the toolbar at the bottom left side.

Real-time fMRI Neurofeedback and BCIs

Turbo-BrainVoyager has been used for advanced real-time applications, especially neurofeedback and brain-computer-interface (BCI) studies. In the TEDx video below, Rainer Goebel introduces these advanced real-time fMRI applications.

Improving Data Quality Online

During real-time processing, Turbo-BrainVoyager runs several routines to improve data quality including motion correction, spatial smoothing and high-pass filtering. If a GPU is availabe, motion correction uses trilinear interpolaton for motion detection and windowed sinc interpolation for final resampling providing high-quality results.
Temporal high-pass filtering is performed by incrementally adding results from motion correction to the design matrix and/or by adding low-frequency drift predictors (see figure on the right). The effect of high-pass filtering can be inspected during real-time operation by toggling between the original and detrended time course. The detrended time course of a vector of voxels (ROI) is used as input for neurofeedback or BCI applications.

Figure showing effect of TBV high-pass filter using discrete cosine transform (DCT) confound predictors. Results of incremental filtering during scanning is shown on the left, while plots on the right show GLM beta fits of DCT confounds.

Screenshot showing neurofeedback option for classifier output. The predicted output signal of a SVM classifier can be used in a bidirectional thermometer.

Multivariate Analysis

Turbo-BrainVoyager supports multi-voxel pattern classification based on the widely used support vector machine (SVM) learning algorithm. Training and testing (prediction) is controlled in two separate steps. First an SVM is trained on the data from one or more completed runs of a real-time session. In subsequent ‘prediction’ runs, trial-by-trial or TR-to-TR real-time classification can be enabled to incrementally predict (ideally with high accuracy) to which class a distributed activity pattern belongs.
While pattern classification is typically used for BCI applications, the classifier output values can also be directly used for neurofeedback applications, for example in TBV’s standard thermometer display of its neurofeedback dialog (see figure on the left).

Selected Publications

Turbo-BrainVoyager has been used for advanced real-time applications, especially neurofeedback and brain-computer-interface (BCI) studies. The publications below from our team provide more details about methods and applications of real-time fMRI neurofeedback and communication BCI applications:

  • Goebel, R., Lührs, M., Ciarlo, A., Esposito, F., & Linden, D. E. (2024). Semantic fMRI neurofeedback of emotions: From basic principles to clinical applications. Philosophical Transactions of the Royal Society B: Biological Sciences, 379(1915), 20230084.
  • Bressler RA, Raible S, Lührs M, Tier R, Goebel R, Linden DE (2023). No threat: Emotion regulation neurofeedback for police special forces recruits. Neuropsychologia, 190, 108699.
  • Ciarlo A, Russo AG, Ponticorvo S, Di Salle F, Lührs M, Goebel R, Esposito F (2022). Semantic fMRI neurofeedback: a multi-subject study at 3 tesla. Journal of Neural Engineering, 19, 036020.
  • Goebel R (2021). Analysis methods for real-time fMRI neurofeedback. In: M. Hampson (Ed.). fMRI Neurofeedback, pp. 23-55. Academic Press.
  • Russo AG, Lührs M, Di Salle F, Esposito F, Goebel R (2021). Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis. Journal of Neural Engineering, 18(4), 046015.
  • B Sorger, R Goebel (2020). Real-time fMRI for brain-computer interfacing. In: Ramsey NF, and Millán JDR, (eds). Brain-Computer Interfaces. San Diego: Elsevier BV, 168, 289-302.
  • Hohenfeld C, Kuhn H, Müller C, Nellessen N, Ketteler S, Heinecke A, Goebel R, Shah NJ, Schulz JB, Reske M, Reetz K. (2020). Changes in brain activation related to visuo-spatial memory after real-time fMRI neurofeedback training in healthy elderly and Alzheimer’s disease. Behav Brain Res. 381, 112435.
  • Goebel R (2019). What’s in the thermometer? Towards semantic neurofeedback at 7 Tesla. Talk in chair symposium of Real-Time Functional Imaging and Neurofeedback (rtFIN) Conference, Maastricht, NL, Dec 9.
  • Kaas A, Goebel R, Valente G, Sorger B.(2019). Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing. Front Hum Neurosci, 13, 427.
  • Krause F, Benjamins C, Eck J, Lührs M, van Hoof R, Goebel R (2019). Active head motion reduction in magnetic resonance imaging using tactile feedback. Hum Brain Mapp, 40, 4026-4037.
  • Skottnik L, Sorger B, Kamp T, Linden D, Goebel R (2019). Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum. Brain Behav, 9, e01240.
  • Lührs M, Riemenschneider B, Eck J, Andonegui AB, Poser BA, Heinecke A, Krause F, Esposito F, Sorger B, Hennig J, Goebel R (2019). The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. Neuroimage, 194, 228-243.
  • Mehler DMA, Sokunbi MO, Habes I, Barawi K, Subramanian L, Range M, Evans J, Hood K, Lührs M, Keedwell P, Goebel R, Linden DEJ (2018). Targeting the affective brain-a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology, 43, 2578-2585.
  • Neyedli HF, Sampaio-Baptista C, Kirkman MA, Havard D, Lührs M, Ramsden K, Flitney DD, Clare S, Goebel R, Johansen-Berg H (2018). Increasing Lateralized Motor Activity in Younger and Older Adults using Real-time fMRI during Executed Movements. Neuroscience, 378, 165-174.
  • Zilverstand A, Sorger B, Slaats-Willemse D, Kan CC, Goebel R, Buitelaar JK (2017). fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study. PLoS One, 12, e0170795.
  • Lührs M, Sorger B, Goebel R, Esposito F (2017). Automated selection of brain regions for real-time fMRI brain-computer interfaces. J Neural Eng, 14, 016004.
  • Krause F, Benjamins C, Lührs M, Eck J, Noirhomme Q, Rosenke M, Brunheim S, Sorger B, Goebel R (2017). Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations. Brain-computer interfaces, 4, 87-101.
  • Zilverstand A, Sorger B, Sarkheil P, Goebel R (2015). fMRI neurofeedback facilitates anxiety regulation in females with spider phobia. Front Behav Neurosci, 9, 148.
  • Sarkheil P, Zilverstand A, Kilian-Hütten N, Schneider F, Goebel R, Mathiak K (2015). fMRI feedback enhances emotion regulation as evidenced by a reduced amygdala response. Behav Brain Res, 281, 326-332.
  • Zilverstand A, Sorger B, Zimmermann J, Kaas A, Goebel R. (2014). Windowed Correlation: A Suitable Tool for Providing Dynamic fMRI-Based Functional Connectivity Neurofeedback on Task Difficulty. PLoS One, 9, e85929.
  • Habes I, Krall SC, Johnston SJ, Yuen KS, Healy D, Goebel R, Sorger B, Linden DE. (2013). Pattern classification of valence in depression. Neuroimage Clinical, 2, 675-83.
  • Sorger, B., Reithler, J., Dahmen, B., Goebel, R. (2012). A Real-time fMRI-based Spelling Device Immediately Enabling Robust Motor-independent Communication. Current Biology, 22, 1333-1338.
  • Linden, D.E.J., Habes, I., Johnston, S.J., Linden, S., Tatineni, R., Subramanian L., Sorger, B., Healy, D., Goebel, R. (2012) Real-time Self-regulation of Emotion Networks in Patients with Depression. PLOS One, 7, e38115.
  • Subramanian, L. Hindle, J.V., Johnston, S., Roberts, M.V., Husain, M., Goebel, R., Linden, D. (2011). Real-Time Functional Magnetic Resonance Imaging Neurofeedback for Treatment of Parkinson’s Disease. The Journal of Neuroscience, 31, 16309-16317.
  • Johnston, S., Linden, D.E., Healy, D., Goebel, R., Habes, I., Boehm, S.G. (2011). Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cognitive, Affective and Behavioral Neuroscience, 11, 44-51.
  • Sorger B, Dahmen B, Reithler J, Gosseries O, Maudoux A, Laureys S, Goebel R (2009). Another kind of ‘BOLD Response’: answering multiple-choice questions via online decoded single-trial brain signals. Prog Brain Res, 177, 275-292.
  • Weiskopf N, Mathiak K, Bock SW, Scharnowski F, Veit R, Grodd W, Goebel R, Birbaumer N (2004). Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Transactions on Biomedical Engineering, 51, 966-970.
  • Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R. & Birbaumer, N. (2003). Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage, 19, 577-586.
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