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Transcranial Magnetic Stimulation

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Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

Journal of affective disorders
BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we p...

Effect of repetitive transcranial magnetic stimulation combined with robot-assisted training on wrist muscle activation post-stroke.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To compare the effects of active assisted wrist extension training, using a robotic exoskeleton (RW), with simultaneous 5 Hz (rTMS + RW) or Sham rTMS (Sham rTMS + RW) over the ipsilesional extensor carpi radialis motor cortical representat...

Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks.

Brain stimulation
BACKGROUND: Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. However, it is difficult to target specific brain regions accurately because the complex anatomy of the b...

Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction te...

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain.

Journal of visualized experiments : JoVE
Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries unique challenges in children. Similarly, transcranial direct current stimulation (tDCS) can improve...

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning.

NeuroImage
The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly gene...

Accuracy of robotic coil positioning during transcranial magnetic stimulation.

Journal of neural engineering
OBJECTIVE: Robotic positioning systems for transcranial magnetic stimulation (TMS) promise improved accuracy and stability of coil placement, but there is limited data on their performance. Investigate the usability, accuracy, and limitations of robo...

Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

Neurorehabilitation and neural repair
. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly ...

Changes in functional connectivity after theta-burst transcranial magnetic stimulation for post-traumatic stress disorder: a machine-learning study.

European archives of psychiatry and clinical neuroscience
Intermittent theta burst stimulation (iTBS) is a novel treatment approach for post-traumatic stress disorder (PTSD), and recent neuroimaging work indicates that functional connectivity profiles may be able to identify those most likely to respond. Ho...