Principles of a Brain-Computer Interface (BCI) Based on Real-Time Functional Magnetic Resonance Imaging (fMRI)

Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
IEEE Transactions on Biomedical Engineering (Impact Factor: 2.35). 07/2004; 51(6):966-70. DOI: 10.1109/TBME.2004.827063
Source: IEEE Xplore


A brain-computer interface (BCI) based on functional magnetic resonance imaging (fMRI) records noninvasively activity of the entire brain with a high spatial resolution. We present a fMRI-based BCI which performs data processing and feedback of the hemodynamic brain activity within 1.3 s. Using this technique, differential feedback and self-regulation is feasible as exemplified by the supplementary motor area (SMA) and parahippocampal place area (PPA). Technical and experimental aspects are discussed with respect to neurofeedback. The methodology now allows for studying behavioral effects and strategies of local self-regulation in healthy and diseased subjects.

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Article: Principles of a Brain-Computer Interface (BCI) Based on Real-Time Functional Magnetic Resonance Imaging (fMRI)

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    • "Its feasibility was largely improved by the development of real-time (rt) fMRI (Cox et al., 1995; Lee et al., 1998; Voyvodic, 1999; Gembris et al., 2000), accomplishing image reconstruction and activation analysis within the acquisition time of a single-volumetric fMRI dataset. Despite the poor temporal resolution of fMRI and the 6-to 8-s latency of the underlying hemodynamic response, several studies demonstrated successful neurofeedback trainings in brain areas, such as the motor cortex (deCharms et al., 2004; Yoo et al., 2008; Berman et al., 2012; Chiew et al., 2012), the anterior cingulate cortex (Weiskopf et al., 2003; Hamilton et al., 2011), the amygdala (Posse et al., 2003; Zotev et al., 2011), the parahippocampal place area, the supplementary motor area (Weiskopf et al., 2004), the auditory cortex (Yoo et al., 2007), and the insular cortex (Caria et al., 2007, 2010; Johnston et al., 2010). Unfortunately, the proof-of-principle nature of most of these reports led to a considerable variation of paradigms and study designs which so far preclude a definite determination and generalization of critical elements for a successful real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training. "
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    ABSTRACT: BACKGROUND: This study investigated the level of self-regulation of the somato-motor cortices (SMC) attained by an extended functional MRI (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line GLM analysis determining the fMRI percent signal changes in the somato-motor cortex (SMC) target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e. the neurofeedback paradigm without the presentation of the feedback signal. Group results show a distinct increase in feedback performance in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the feedback performance showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left and right hand imagery, with a slight indication of more ipsilateral deactivation in the early right hand trainings.
    Frontiers in Human Neuroscience 10/2015; 9. DOI:10.3389/fnhum.2015.00547 · 3.63 Impact Factor
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    • "People can use a BCI to interact with their environments even if they have limited or no muscle control. Various data acquisition techniques like electroencephalography (EEG) (Wolpaw et al., 2002), electrocorticography (ECoG) (Leuthardt et al., 2004), functional magnetic resonance imaging (fMRI) (Weiskopf et al., 2004), and near infrared spectroscopy (NIRS) (Coyle et al., 2004) can be used as a BCI system. "
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    ABSTRACT: A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed.
    Frontiers in Systems Neuroscience 08/2014; 8:139. DOI:10.3389/fnsys.2014.00139
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    • "The neurofeedback setup used Turbo-BrainVoyager (Brain Innovation, Maastricht, The Netherlands), custom real-time image export tools programmed in ICE VA25 (Siemens Healthcare) [31], and custom scripts running in MATLAB (Mathworks Inc., Natick, MA, USA). This allowed participants to be shown visual representations of BOLD signal changes in specific brain regions (in the form of a thermometer display projected into the scanner) with a delay of less than 2 s from the acquisition of the image. "
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    ABSTRACT: Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.
    PLoS ONE 03/2014; 9(3):e91090. DOI:10.1371/journal.pone.0091090 · 3.23 Impact Factor
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