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Bro, Viktor
Publications (6 of 6) Show all publications
Medvedev, A., Cubo, R., Olsson, F., Bro, V. & Andersson, H. (2019). Control-Engineering Perspective on Deep Brain Stimulation: Revisited. In: : . Paper presented at 2019 American Control Conference (ACC).
Open this publication in new window or tab >>Control-Engineering Perspective on Deep Brain Stimulation: Revisited
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Deep brain stimulation (DBS) is a an established therapy in neurological and mental disorders making use of electrical pulses chronically delivered to a certain disease-specific neural target through surgically implanted electrodes. The therapeutical effect of DBS is highly individual and depends on the target coverage by the stimuli and the amount of spill beyond it. This can be suitably formulated as an optimization problem. Since the biological mechanism underlying the DBS therapy is mainly unknown, and due to high inter-patient and intra-patient variability of the DBS effect, a pragmatic approach to the DBS programming is to consider the process as tuning of a control system for the symptoms. Such a technology assumes that the symptoms are accurately quantified. The paper summarizes the progress in the individualized DBS and presents the results of a limited clinical study making use of the proposed DBS programming approach.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-397991 (URN)10.23919/ACC.2019.8814839 (DOI)
Conference
2019 American Control Conference (ACC)
Available from: 2019-11-29 Created: 2019-11-29 Last updated: 2019-11-29
Bro, V. & Medvedev, A. (2019). Identification of continuous Volterra models with explicit time delay through series of Laguerre functions. In: Proc. 58th Conference on Decision and Control: . Paper presented at CDC 2019, December 11–13, Nice, France. IEEE
Open this publication in new window or tab >>Identification of continuous Volterra models with explicit time delay through series of Laguerre functions
2019 (English)In: Proc. 58th Conference on Decision and Control, IEEE, 2019Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-385943 (URN)
Conference
CDC 2019, December 11–13, Nice, France
Note

to appear

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-11-21Bibliographically approved
Bro, V. & Medvedev, A. (2019). Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters.
Open this publication in new window or tab >>Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters
2019 (English)In: Article in journal (Other academic) Submitted
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-385940 (URN)
Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved
Bro, V. (2019). Volterra modeling of the human smooth pursuit system in health and disease. (Licentiate dissertation). Uppsala University
Open this publication in new window or tab >>Volterra modeling of the human smooth pursuit system in health and disease
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis treats the identification of Volterra models of the human smooth pursuit system from eye-tracking data. Smooth pursuit movements are gaze movements used in tracking of moving targets and controlled by a complex biological network involving the eyes and brain. Because of the neural control of smooth pursuit, these movements are affected by a number of neurological and mental conditions, such as Parkinson's disease. Therefore, by constructing mathematical models of the smooth pursuit system from eye-tracking data of the patient, it may be possible to identify symptoms of the disease and quantify them. While the smooth pursuit dynamics are typically linear in healthy subjects, this is not necessarily true in disease or under influence of drugs. The Volterra model is a classical black-box model for dynamical systems with smooth nonlinearities that does not require much a priori information about the plant and thus suitable for modeling the smooth pursuit system.

The contribution of this thesis is mainly covered by the four appended papers. Papers I–III treat the problem of reducing the number of parameters in Volterra models with the kernels parametrized in Laguerre functional basis (Volterra–Laguerre models), when utilizing them to capture the signal form of smooth pursuit movements. Specifically, a Volterra–Laguerre model is obtained by means of sparse estimation and principal component analysis in Paper I, and a Wiener model approach is used in Paper II. In Paper III, the same model as in Paper I is considered to examine the feasibility of smooth pursuit eye tracking for biometric purposes. Paper IV is concerned with a Volterra–Laguerre model that includes an explicit time delay. An approach to the joint estimation of the time delay and the finite-dimensional part of the Volterra model is proposed and applied to time-delay compensation in eye-tracking data.

Place, publisher, year, edition, pages
Uppsala University, 2019
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2019-003
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-385951 (URN)
Supervisors
Available from: 2019-05-08 Created: 2019-06-18 Last updated: 2019-06-18Bibliographically approved
Bro, V. & Medvedev, A. (2017). Constrained SPICE in Volterra–Laguerre modeling of human smooth pursuit. In: Proc. 1st Conference on Control Technology and Applications: . Paper presented at CCTA 2017, August 27–30, Mauna Lani, HI (pp. 13-18). IEEE
Open this publication in new window or tab >>Constrained SPICE in Volterra–Laguerre modeling of human smooth pursuit
2017 (English)In: Proc. 1st Conference on Control Technology and Applications, IEEE, 2017, p. 13-18Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-334955 (URN)10.1109/CCTA.2017.8062433 (DOI)000426981500003 ()978-1-5090-2182-6 (ISBN)
Conference
CCTA 2017, August 27–30, Mauna Lani, HI
Funder
Vinnova
Available from: 2017-10-09 Created: 2017-11-29 Last updated: 2019-06-18Bibliographically approved
Bro, V. & Medvedev, A. (2017). Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation. In: Proc. 56th Conference on Decision and Control: . Paper presented at CDC 2017, December 12–15, Melbourne, Australia (pp. 4692-4697). IEEE
Open this publication in new window or tab >>Nonlinear dynamics of the human smooth pursuit system in health and disease: Model structure and parameter estimation
2017 (English)In: Proc. 56th Conference on Decision and Control, IEEE, 2017, p. 4692-4697Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-334957 (URN)10.1109/CDC.2017.8264352 (DOI)000424696904084 ()978-1-5090-2873-3 (ISBN)
Conference
CDC 2017, December 12–15, Melbourne, Australia
Funder
Vinnova
Available from: 2018-01-23 Created: 2017-11-29 Last updated: 2019-06-18Bibliographically approved
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