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Bci interface fmri

Bettina Sorger, Tabea Kamp, Nikolaus Weiskopf, Judith Caroline Peters, Rainer Goebel
Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i...
September 19, 2016: Neuroscience
Teresa Sousa, Bruno Direito, João Lima, Carlos Ferreira, Urbano Nunes, Miguel Castelo-Branco
A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class. The increase in the number of levels of control in BCI applications may allow for larger efficiency in neurofeedback applications. In this work we test the hypothesis whether more than two modulation levels can be achieved in a single brain region, the hMT+/V5 complex...
2016: PloS One
A A Frolov, D Husek, A V Silchenko, Y Tintera, J Rydlo
With the use of functional MRI (fMRI), we studied the changes in brain hemodynamic activity of healthy subjects during motor imagery training with the use brain-computer interface (BCI), which is based on the recognition of EEG patterns of imagined movements. ANOVA dispersion analysis showed there are 14 areas of the brain where statistically sgnificant changes were registered. Detailed analysis of the activity in these areas before and after training (Student's and Mann-Whitney tests) reduced the amount of areas with significantly changed activity to five; these are Brodmann areas 44 and 45, insula, middle frontal gyrus, and anterior cingulate gyrus...
January 2016: Fiziologiia Cheloveka
Korhan Buyukturkoglu, Hans Roettgers, Jens Sommer, Mohit Rana, Leonie Dietzsch, Ezgi Belkis Arikan, Ralf Veit, Rahim Malekshahi, Tilo Kircher, Niels Birbaumer, Ranganatha Sitaram, Sergio Ruiz
INTRODUCTION: Obsessive-compulsive disorder (OCD) is a common and chronic condition that can have disabling effects throughout the patient's lifespan. Frequent symptoms among OCD patients include fear of contamination and washing compulsions. Several studies have shown a link between contamination fears, disgust over-reactivity, and insula activation in OCD. In concordance with the role of insula in disgust processing, new neural models based on neuroimaging studies suggest that abnormally high activations of insula could be implicated in OCD psychopathology, at least in the subgroup of patients with contamination fears and washing compulsions...
2015: PloS One
Catharina Zich, Stefan Debener, Cornelia Kranczioch, Martin G Bleichner, Ingmar Gutberlet, Maarten De Vos
Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain-computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas...
July 1, 2015: NeuroImage
A A Frolov, D Gusek, P D Bobrov, O A Mokienko, L A Chernikova, R N Konovalov
Studied are sources of brain activity contributing to EEG patterns which correspond to motor imagery. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) allowing for controlling external technical devices directly by brain signals without involving muscle activity. Sources of brain activity are identified by Independent Component Analysis. Those independent components for which the BCI classification accuracy are at maximum are treated as relevant for motor imagery task...
May 2014: Fiziologiia Cheloveka
Sebastian Baecke, Ralf Lützkendorf, Johannes Mallow, Michael Luchtmann, Claus Tempelmann, Jörg Stadler, Johannes Bernarding
Real-time functional Magnetic Resonance Imaging (rtfMRI) is used mainly for neurofeedback or for brain-computer interfaces (BCI). But multi-site rtfMRI could in fact help in the application of new interactive paradigms such as the monitoring of mutual information flow or the controlling of objects in shared virtual environments. For that reason, a previously developed framework that provided an integrated control and data analysis of rtfMRI experiments was extended to enable multi-site rtfMRI. Important new components included a data exchange platform for analyzing the data of both MR scanners independently and/or jointly...
2015: Scientific Reports
Jacques Luauté, Dominique Morlet, Jérémie Mattout
The reestablishment of communication is one of the main goals for patients with disorders of consciousness (DOC). It is now established that many DOC patients retain the ability to process stimuli of varying complexity even in the absence of behavioural response. Motor impairment, fatigue, attention disorders might contribute to the difficulty of communication in this population. Brain-computer interfaces (BCI) might be helpful in restoring some communication ability in these patients. After a definition of the different disorders of consciousness that might benefit from BCI, brain markers able to detect cognitive processes and awareness in the absence of behavioural manifestation are described...
February 2015: Annals of Physical and Rehabilitation Medicine
Alyssa M Batula, Hasan Ayaz, Youngmoo E Kim
This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface, with two subjects achieving 50% accuracy. fNIRS is a noninvasive, safe, portable, and affordable optical brain imaging technique used to monitor cortical hemodynamic changes...
2014: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Moses O Sokunbi, David E J Linden, Isabelle Habes, Stephen Johnston, Niklas Ihssen
Here we present a novel neurofeedback subsystem for the presentation of motivationally relevant visual feedback during the self-regulation of functional brain activation. Our "motivational neurofeedback" approach uses functional magnetic resonance imaging (fMRI) signals elicited by visual cues (pictures) and related to motivational processes such as craving or hunger. The visual feedback subsystem provides simultaneous feedback through these images as their size corresponds to the magnitude of fMRI signal change from a target brain area...
2014: Frontiers in Behavioral Neuroscience
Jie Song, Brittany M Young, Zack Nigogosyan, Leo M Walton, Veena A Nair, Scott W Grogan, Mitchell E Tyler, Dorothy Farrar-Edwards, Kristin E Caldera, Justin A Sattin, Justin C Williams, Vivek Prabhakaran
The relationship of the structural integrity of white matter tracts and cortical activity to motor functional outcomes in stroke patients is of particular interest in understanding mechanisms of brain structural and functional changes while recovering from stroke. This study aims to probe these underlying mechanisms using diffusion tensor imaging (DTI) and fMRI measures. We examined the structural integrity of the posterior limb of the internal capsule (PLIC) using DTI and corticomotor activity using motor-task fMRI in stroke patients who completed up to 15 sessions of rehabilitation therapy using Brain-Computer Interface (BCI) technology...
2014: Frontiers in Neuroengineering
Linda van der Heiden, Giulia Liberati, Ranganatha Sitaram, Sunjung Kim, Piotr Jaśkowski, Antonino Raffone, Marta Olivetti Belardinelli, Niels Birbaumer, Ralf Veit
In order to enable communication through a brain-computer interface (BCI), it is necessary to discriminate between distinct brain responses. As a first step, we probed the possibility to discriminate between affirmative ("yes") and negative ("no") responses using a semantic classical conditioning paradigm, within an fMRI setting. Subjects were presented with congruent and incongruent word-pairs as conditioned stimuli (CS), respectively eliciting affirmative and negative responses. Incongruent word-pairs were associated to an unpleasant unconditioned stimulus (scream, US1) and congruent word-pairs were associated to a pleasant unconditioned stimulus (baby-laughter, US2), in order to elicit emotional conditioned responses (CR)...
2014: Frontiers in Behavioral Neuroscience
Brittany M Young, Zack Nigogosyan, Léo M Walton, Jie Song, Veena A Nair, Scott W Grogan, Mitchell E Tyler, Dorothy F Edwards, Kristin Caldera, Justin A Sattin, Justin C Williams, Vivek Prabhakaran
This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI) technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n = 8) or no therapy (n = 6). Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT) as well as task-based fMRI scans were conducted before, during, after, and 1 month after therapy administration or at analogous intervals in the absence of therapy...
2014: Frontiers in Neuroengineering
Brittany Mei Young, Zack Nigogosyan, Alexander Remsik, Léo M Walton, Jie Song, Veena A Nair, Scott W Grogan, Mitchell E Tyler, Dorothy Farrar Edwards, Kristin Caldera, Justin A Sattin, Justin C Williams, Vivek Prabhakaran
Brain-computer interface (BCI) technology is being incorporated into new stroke rehabilitation devices, but little is known about brain changes associated with its use. We collected anatomical and functional MRI of nine stroke patients with persistent upper extremity motor impairment before, during, and after therapy using a BCI system. Subjects were asked to perform finger tapping of the impaired hand during fMRI. Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) domains of Hand Function (HF) and Activities of Daily Living (ADL) were also assessed...
2014: Frontiers in Neuroengineering
Takashi Ono, Yutaka Tomita, Manabu Inose, Tetsuo Ota, Akio Kimura, Meigen Liu, Junichi Ushiba
Electroencephalogram-based brain-computer interfaces (BCI) have been used as a potential tool for training volitional regulation of corticomuscular drive in patients who have severe hemiplegia due to stroke. However, it is unclear whether ERD observed while attempting motor execution can be regarded as a neural marker that represents M1 excitability in survivors of severe stroke. Therefore we investigated the association between ERD and the blood-oxygen-level-dependent (BOLD) fMRI signal during attempted movement of a paralyzed finger in stroke patients...
March 2015: Brain Topography
Thierry Castermans, Matthieu Duvinage, Guy Cheron, Thierry Dutoit
In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile...
2013: Brain Sciences
Kouji Takano, Hiroki Ora, Kensuke Sekihara, Sunao Iwaki, Kenji Kansaku
The visual P300 brain-computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG-fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas...
2014: Frontiers in Neurology
Jane E Huggins, Jonathan R Wolpaw
No abstract text is available yet for this article.
June 2014: Journal of Neural Engineering
Ori Cohen, Moshe Koppel, Rafael Malach, Doron Friedman
OBJECTIVE: We have developed a brain-computer interface (BCI) system based on real-time functional magnetic resonance imaging (fMRI) with virtual reality feedback. The advantage of fMRI is the relatively high spatial resolution and the coverage of the whole brain; thus we expect that it may be used to explore novel BCI strategies, based on new types of mental activities. However, fMRI suffers from a low temporal resolution and an inherent delay, since it is based on a hemodynamic response rather than electrical signals...
June 2014: Journal of Neural Engineering
Jennifer L Collinger, Ramana Vinjamuri, Alan D Degenhart, Douglas J Weber, Gustavo P Sudre, Michael L Boninger, Elizabeth C Tyler-Kabara, Wei Wang
After spinal cord injury (SCI), motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation (AO), in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, AO can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI) even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment...
2014: Frontiers in Integrative Neuroscience
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