scholarly journals Mapping of primary somatosensory cortex of the hand area using a high-density electrocorticography grid for closed-loop brain computer interface.

2020 ◽  
Author(s):  
Daniel Richard Kramer ◽  
Morgan Brianna Lee ◽  
Michael Barbaro ◽  
Angad S. Gogia ◽  
Terrance Peng ◽  
...  
Keyword(s):  
Somatosensory Cortex ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
High Density ◽  
Computer Interface ◽  
Primary Somatosensory Cortex ◽  
Hand Area

scholarly journals Locating primary somatosensory cortex in human brain stimulation studies: systematic review and meta-analytic evidence

2019 ◽  
Vol 121 (1) ◽  
pp. 152-162 ◽  
Author(s):  
Nicholas Paul Holmes ◽  
Luigi Tamè
Keyword(s):  
Somatosensory Cortex ◽  
Primary Motor Cortex ◽  
Primary Somatosensory Cortex ◽  
Similar Data ◽  
Indirect Methods ◽  
Gold Standard Method ◽  
Systematic Assessment ◽  
Stereotactic Navigation ◽  
Scalp Location ◽  
Hand Area

Transcranial magnetic stimulation (TMS) over human primary somatosensory cortex (S1), unlike over primary motor cortex (M1), does not produce an immediate, objective output. Researchers must therefore rely on one or more indirect methods to position the TMS coil over S1. The “gold standard” method of TMS coil positioning is to use individual functional and structural magnetic resonance imaging (f/sMRI) alongside a stereotactic navigation system. In the absence of these facilities, however, one common method used to locate S1 is to find the scalp location that produces twitches in a hand muscle (e.g., the first dorsal interosseus, M1-FDI) and then move the coil posteriorly to target S1. There has been no systematic assessment of whether this commonly reported method of finding the hand area of S1 is optimal. To do this, we systematically reviewed 124 TMS studies targeting the S1 hand area and 95 fMRI studies involving passive finger and hand stimulation. Ninety-six TMS studies reported the scalp location assumed to correspond to S1-hand, which was on average 1.5–2 cm posterior to the functionally defined M1-hand area. Using our own scalp measurements combined with similar data from MRI and TMS studies of M1-hand, we provide the estimated scalp locations targeted in these TMS studies of the S1-hand. We also provide a summary of reported S1 coordinates for passive finger and hand stimulation in fMRI studies. We conclude that S1-hand is more lateral to M1-hand than assumed by the majority of TMS studies.

scholarly journals An Implantable Brain-Computer Interface for Investigation of Novel Closed-Loop Therapies

2018 ◽  
Vol 12 ◽  
Author(s):  
Martin Schüttler ◽  
Fabian Kohler ◽  
Christian Stolle ◽  
Jörg Fischer ◽  
Thomas Stieglitz ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals A closed-loop, music-based brain-computer interface for emotion mediation

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0213516 ◽  
Author(s):  
Stefan K. Ehrlich ◽  
Kat R. Agres ◽  
Cuntai Guan ◽  
Gordon Cheng
Keyword(s):  
Closed Loop ◽  
Brain Computer Interface ◽  
Computer Interface

A Closed-Loop Brain–Computer Interface Triggering an Active Ankle–Foot Orthosis for Inducing Cortical Neural Plasticity

2014 ◽  
Vol 61 (7) ◽  
pp. 2092-2101 ◽  
Author(s):  
Ren Xu ◽  
Ning Jiang ◽  
Natalie Mrachacz-Kersting ◽  
Chuang Lin ◽  
Guillermo Asin Prieto ◽  
...  
Keyword(s):  
Neural Plasticity ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Ankle Foot Orthosis ◽  
Foot Orthosis

scholarly journals Utility and lower limits of frequency detection in surface electrode stimulation for somatosensory brain-computer interface in humans

2020 ◽  
Vol 48 (2) ◽  
pp. E2
Author(s):  
Daniel R. Kramer ◽  
Krista Lamorie-Foote ◽  
Michael Barbaro ◽  
Morgan B. Lee ◽  
Terrance Peng ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Limit Of Detection ◽  
Brain Computer Interface ◽  
Stimulation Frequency ◽  
Computer Interface ◽  
Stimulation Parameter ◽  
Frequency Detection ◽  
Stimulation Parameters ◽  
Lower Limits ◽  
Hand Area

OBJECTIVEStimulation of the primary somatosensory cortex (S1) has been successful in evoking artificial somatosensation in both humans and animals, but much is unknown about the optimal stimulation parameters needed to generate robust percepts of somatosensation. In this study, the authors investigated frequency as an adjustable stimulation parameter for artificial somatosensation in a closed-loop brain-computer interface (BCI) system.METHODSThree epilepsy patients with subdural mini-electrocorticography grids over the hand area of S1 were asked to compare the percepts elicited with different stimulation frequencies. Amplitude, pulse width, and duration were held constant across all trials. In each trial, subjects experienced 2 stimuli and reported which they thought was given at a higher stimulation frequency. Two paradigms were used: first, 50 versus 100 Hz to establish the utility of comparing frequencies, and then 2, 5, 10, 20, 50, or 100 Hz were pseudorandomly compared.RESULTSAs the magnitude of the stimulation frequency was increased, subjects described percepts that were “more intense” or “faster.” Cumulatively, the participants achieved 98.0% accuracy when comparing stimulation at 50 and 100 Hz. In the second paradigm, the corresponding overall accuracy was 73.3%. If both tested frequencies were less than or equal to 10 Hz, accuracy was 41.7% and increased to 79.4% when one frequency was greater than 10 Hz (p = 0.01). When both stimulation frequencies were 20 Hz or less, accuracy was 40.7% compared with 91.7% when one frequency was greater than 20 Hz (p < 0.001). Accuracy was 85% in trials in which 50 Hz was the higher stimulation frequency. Therefore, the lower limit of detection occurred at 20 Hz, and accuracy decreased significantly when lower frequencies were tested. In trials testing 10 Hz versus 20 Hz, accuracy was 16.7% compared with 85.7% in trials testing 20 Hz versus 50 Hz (p < 0.05). Accuracy was greater than chance at frequency differences greater than or equal to 30 Hz.CONCLUSIONSFrequencies greater than 20 Hz may be used as an adjustable parameter to elicit distinguishable percepts. These findings may be useful in informing the settings and the degrees of freedom achievable in future BCI systems.

scholarly journals Distinct effects of amyloid and tau deposition on eigenvector centrality during hippocampal down-regulation: a real-time fMRI virtual reality closed-loop neurofeedback study with CSF biomarkers

2019 ◽  
Author(s):  
Stavros Skouras ◽  
Jordi Torner ◽  
Patrik Andersson ◽  
Yury Koush ◽  
Carles Falcon ◽  
...  
Keyword(s):  
At Risk ◽  
Real Time ◽  
Somatosensory Cortex ◽  
Resting State ◽  
Closed Loop ◽  
Self Regulation ◽  
Primary Somatosensory Cortex ◽  
Csf Biomarkers ◽  
Eigenvector Centrality ◽  
Down Regulation

AbstractHippocampal down-regulation is associated with genetic predisposition to Alzheimer’s disease (AD), neurodevelopmental processes and disease symptoms. Resting state eigenvector centrality (EC) patterns resemble those of FDG-PET in AD, they can predict self-regulation performance and they are related to functional compensation across the pathophysiological continuum of AD. We acquired cerebrospinal fluid (CSF) biomarkers from a cognitively unimpaired sample at risk for AD (N=48), to investigate the effect of β- amyloid peptide 42 (Aβ42) and phosphorylated tau (p-Tau) levels on EC during the down-regulation of hippocampal subfield cornu ammonis 1, with real-time fMRI closed-loop neurofeedback. Controlling the effects of confounding variables (age, sex, number of APOE ε4 alleles, cognitive reserve, brain reserve and hippocampal down-regulation performance), CSF Aβ42 levels correlated positively with EC in the anterior cingulate cortex (BA24, BA32) and primary motor cortex (BA4). CSF p-Tau levels correlated with EC positively in the ACC (BA32, BA10) ventral striatum (caudate, nucleus accumbens, putamen) and left primary somatosensory cortex (BA2), as well as negatively in the posterior cingulate cortex, precuneus, cuneus and left frontal pole (BA9). Controlling for CSF biomarkers and other prognosis variables, age correlated negatively with EC in the midcingulate cortex, insula, primary somatosensory cortex (BA2) and inferior parietal lobule (BA40), as well as positively with EC in the inferior temporal gyri. Taken together, we identified patterns of functional connectomics in individuals at risk of AD during hippocampal down-regulation, which resemble those found during resting state at advanced AD stages. Moreover, we provide a standard paradigm to replicate and extend this work on a global level. This opens new avenues for further research applications, which quantify and monitor disease progression, by identifying early alterations in the self-regulation of brain function, with potential for non-invasive prognostic screening.HighlightsACC centrality decreases with early Aβ42ACC centrality increases with p-TauPCC centrality decreases with p-TauMCC centrality decreases in healthy aging

scholarly journals Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

2021 ◽  
Vol 15 ◽  
Author(s):  
Neethu Robinson ◽  
Tushar Chouhan ◽  
Ernest Mihelj ◽  
Paulina Kratka ◽  
Frédéric Debraine ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Computer Interface ◽  
Brain Activation ◽  
Classification Performance ◽  
Open Loop ◽  
Computer Interface ◽  
Single Subject ◽  
User Engagement

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

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