M
Mohammad Daneshzand
Researcher at Harvard University
Publications - 24
Citations - 206
Mohammad Daneshzand is an academic researcher from Harvard University. The author has contributed to research in topics: Electromagnetic coil & Deep brain stimulation. The author has an hindex of 7, co-authored 18 publications receiving 111 citations. Previous affiliations of Mohammad Daneshzand include University of Bridgeport.
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Journal ArticleDOI
Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation
TL;DR: A reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics is developed, able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential recordings of the PD state.
Journal ArticleDOI
A 3-axis coil design for multichannel TMS arrays.
Lucia I. Navarro de Lara,Mohammad Daneshzand,Anthony Mascarenas,Douglas Paulson,Kevin Pratt,Yoshio Okada,Tommi Raij,Sergey N. Makarov,Aapo Nummenmaa +8 more
TL;DR: The performance of a novel modular 3-axis TMS coil that can be utilized as a unit element in large-scale multichannel TMS arrays and the air-cooling system was effective for brief high-frequency pulse trains and extended single- and paired-pulse TMS protocols.
Journal ArticleDOI
Computational Stimulation of the Basal Ganglia Neurons with Cost Effective Delayed Gaussian Waveforms.
TL;DR: The Gaussian Delay Gaussian (GDG) waveforms achieved lower number of misses in eliciting action potential while having a lower amplitude and shorter length of delay compared to numerous different pulse shapes and were able to reduce the synchronization of GPi neurons more effectively than any other waveform.
Journal ArticleDOI
A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB implementation
Sergey N. Makarov,William A. Wartman,Mohammad Daneshzand,Kyoko Fujimoto,Tommi Raij,Aapo Nummenmaa +5 more
TL;DR: The previously proposed BEM-FMM algorithm has been improved in several novel ways, resulting in a threefold increase in computational speed while maintaining the same solution accuracy.
Proceedings ArticleDOI
FPGA-based denoising and beat detection of the ECG signal
TL;DR: The hardware system has achieved an overall accuracy of 98% in the beat detection phase, while providing the detected beats and the classification of irregular heart beat rates in real time.