Brain-mapping using robotized TMS

Finke, Markus; Fadini, Tommaso; Kantelhardt, Sven R.; Giese, Alf; Matthäus, Lars; Schweikard, Achim

doi:10.1109/iembs.2008.4650069

Cited by 17 publications

(16 citation statements)

References 7 publications

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“…However, Equation 2-Equation 6 show that there is a trade-off among the RSIs. For example, a larger motor arm generally leads to a decrease in the motor speed but also to an increased motor torque and error.…”

Section: Evaluation and Comparisonmentioning

confidence: 99%

“…Several conventional robots have been used earlier for TMS, such as the NeuroMate (ISS/IMMI, Sacremento, CA) [3,4], Adept Viper s850 (Adept Technology, Inc. Livermore, CA, USA) [5,6], and Kuka KR3 (Ausburg, Germany) [7]. Most systems use optical tracking of the robot and the subjects head to let the robot position the coil over the desired location on the scalp.…”

Section: Introductionmentioning

confidence: 99%

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A method for evaluation and comparison of parallel robots for safe human interaction, applied to robotic TMS

Jong

Stienen

Wijk

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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Abstract-Transcranial magnetic stimulation (TMS) is a noninvasive method to modify behaviour of neurons in the brain. TMS is applied by running large currents through a coil close to the scalp. For consistent results it is required to maintain the coil position within millimetres of the targeted location, but natural head sway and practitioner fatigue may hinder this.Serial robots are currently used to assist the application of TMS. However, their low stiffness limits the performance and their high moving mass limits the operating speeds for safety reasons. Since 6-DOF parallel robots combine high stiffness with low moving mass, they have potential for fast, safe and accurate positioning required for TMS during activities.For choosing the safest parallel manipulator, we developed an evaluation method using robotic safety criteria, including motor speed, motor acceleration, force transmission, and workspace accuracy. The method is applied for evaluation and comparison of the Delta robot with rotation head, the Hexaglide robot, and the Hexa robot. The Hexa robot shows to have the best safety characteristics.This paper also presents the design and evaluation of four controller strategies for the Hexa robot. These strategies include the application of a straightforward PID control (PID), a PID control with Jacobian Feed-Forward (PID J), a PID control with a Spring Force Control (PID O + ) and a combination of these (PID J O + ).

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Section: Evaluation and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method for evaluation and comparison of parallel robots for safe human interaction, applied to robotic TMS

Jong

Stienen

Wijk

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

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“…Strategies such as CIT and weight‐bearing therapy by using robotics, combined with stimulation techniques such as tDCS or rTMS, have shown promise within the literature but not always consistent results [104]. These combined strategies appear to increase the efficacies of standard treatments by either increasing their accuracy (with respect to physical therapy) or by modulating the cortical excitability, thus potentially increasing neural plasticity [102,103,105,106].…”

Section: Tms Combined With Robotics and Pharmacotherapymentioning

confidence: 99%

Assessment and Modulation of Neural Plasticity in Rehabilitation With Transcranial Magnetic Stimulation

Bashir

Mizrahi

Weaver

et al. 2010

PM&R

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Despite intensive efforts towards the improvement of outcomes after acquired brain injury functional recovery is often limited. One reasons is the challenge in assessing and guiding plasticity after brain injury. In this context, Transcranial Magnetic Stimulation (TMS) - a noninvasive tool of brain stimulation - could play a major role. TMS has shown to be a reliable tool to measure plastic changes in the motor cortex associated with interventions in the motor system; such as motor training and motor cortex stimulation. In addition, as illustrated by the experience in promoting recovery from stroke, TMS a promising therapeutic tool to minimize motor, speech, cognitive, and mood deficits. In this review, we will focus on stroke to discuss how TMS can provide insights into the mechanisms of neurological recovery, and can be used for measurement and modulation of plasticity after an acquired brain insult.

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“…In order to standardize procedures and consequently reduce inter-subject variability in response to TMS, the field has recently embraced major technological evolutions. Neuronavigation systems dedicated to TMS (Herwig et al, 2001) significantly improved its spatial precision and reproducibility (Julkunen et al, 2009 ;Weiss et al, 2013) and TMS-robotized systems enabled the automation of coil positioning (Finke et al, 2008 ;Kantelhardt et al, 2009 ;Ginhoux et al, 2013). In addition to improving spatial precision and reproducibility compared to manual positioning (Ginhoux et al, 2013), robotized TMS paves the way for new acquisition protocols, such as automated cortical mapping procedures (Harquel et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

Automatized set-up procedure for transcranial magnetic stimulation protocols

et al. 2017

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Transcranial Magnetic Stimulation (TMS) established itself as a powerful technique for probing and treating the human brain. Major technological evolutions, such as neuronavigation and robotized systems, have continuously increased the spatial reliability and reproducibility of TMS, by minimizing the influence of human and experimental factors. However, there is still a lack of efficient set-up procedure, which prevents the automation of TMS protocols. For example, the set-up procedure for defining the stimulation intensity specific to each subject is classically done manually by experienced practitioners, by assessing the motor cortical excitability level over the motor hotspot (HS) of a targeted muscle. This is time-consuming and introduces experimental variability. Therefore, we developed a probabilistic Bayesian model (AutoHS) that automatically identifies the HS position. Using virtual and real experiments, we compared the efficacy of the manual and automated procedures. AutoHS appeared to be more reproducible, faster, and at least as reliable as classical manual procedures. By combining AutoHS with robotized TMS and automated motor threshold estimation methods, our approach constitutes the first fully automated set-up procedure for TMS protocols. The use of this procedure decreases inter-experimenter variability while facilitating the handling of TMS protocols used for research and clinical routine.

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Brain-mapping using robotized TMS

Cited by 17 publications

References 7 publications

A method for evaluation and comparison of parallel robots for safe human interaction, applied to robotic TMS

A method for evaluation and comparison of parallel robots for safe human interaction, applied to robotic TMS

Assessment and Modulation of Neural Plasticity in Rehabilitation With Transcranial Magnetic Stimulation

Automatized set-up procedure for transcranial magnetic stimulation protocols

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