Dr Tatiana Kameneva
PhD, The University of Melbourne, Australia; Masters, Kazakh National University, Kazakhstan; Bachelor Degree, Kazakh National University, Kazakhstan
- School of Software and Electrical Engineering
- Engineering Building, Level 5, Office 506a Hawthorn campus
- ORCID profile
Tatiana Kameneva is Associate Professor at Swinburne University of Technology. She has extensive research experience in neuroscience, computational modelling, applied mathematics, and biomedical engineering. Tatiana contributes to advances in understanding of information processing in neural tissue, investigates how electrical stimuli affect neural response, and works on the development of new stimulation methods that can be used across a broad range of medical bionics applications.
Medical Biophysics; Neuroscience; Biotechnology; Mathematical biology
PhD candidate and honours supervision
Higher degrees by research
Accredited to supervise Masters & Doctoral students as Principal Supervisor.
PhD topics and outlines
Biophysical modelling of nanoparticle-enhanced infrared neural stimulation : In visually impaired people who have lost photoreceptors, a sensation of light can be elicited by stimulating surviving retinal ganglion cells (RGCs). Optical stimulation techniques have been shown to achieve significant improvements in the stimulus resolution and inhibition of RGCs. The aim of this project is to build biophysical models of nanoparticle-enhanced infrared stimulation of RGCs.
Deep reinforcement learning for human-centric guidance in AR/VR: A branch of machine learning called deep reinforcement learning has recently outperformed human experts on tasks such as playing Atari games and the traditional Chinese game Go. In this project, we seek a student to research and develop a deep reinforcement-learning framework for learning task-specific visual enhancement filters, and from this, novel scene visualisations to guide human activity.
Functional role of beta band frequency oscillations in humans: The field of brain-machine interfaces rapidly grows. New neural decoding algorithms are proposed to control a robotic arm or a wheel chair. Recorded power in beta oscillations (10-45 Hz) may be used to detect the patient’s attention and readiness to make a movement; therefore, enhancing the existing decoding algorithms. The project would suit someone with an interest in signal processing.
Gender and stature identification using image processing and machine learning algorithms: Footprints are unique characteristics of a person and differ in shape, pattern, margin, and toe marks. In this project, the student will collect data and then develop a machine learning algorithm for automatic identification of gender, age, height and weight of a person from their footprints.
Identifying brain pathways regulating sympathetic nervous outflow: Hypertension is a major cause of death and disability from stroke and dementia. Specific brain regions drive the excess sympathetic outflow in the various forms of hypertension. The student will analyse brain signals and sympathetic nervous outflow collected simultaneously, using magnetoencephalography (MEG) and muscle sympathetic nerve activity (MSNA) techniques.
Investigation of the effects of electrical and light stimulation on neural response: Electrical stimulation has been used to restore sensory functions in people who lost their vision or hearing. A novel way to stimulate neurons is to combine conventional electrical stimulation with targeted optical stimulation. The aim of this project is to explore the effects of electrical and light stimulation on neural responses in experiments and in computer simulations.
Nanoparticle enhanced infrared neural stimulation of retinal ganglion cells: In this project, the student will study experimentally how retinal ganglion cells respond to combined optical and electrical stimulation, when in vitro bath is enriched with gold nanoparticles.
Seizure forecasting: Electroencephalography (EEG) is often used to predict a seizure, with varying success between participants. There is an increasing interest to use non-EEG body signals, including electrocardiogram (ECG) to help with seizures detection and prediction. The aim of this project is to use advanced signal processing to forecast seizures from EEG and ECG data recorded by Seer Medical.
Using machine learning techniques to explore differences in physiology between people with syncope and healthy controls: Syncope occurs as temporary loss of consciousness caused by a fall in blood pressure. To develop an appropriate treatment for the condition, it is important to understand underlying physiology of syncope. The aim of the project is to use machine learning techniques to explore differences in physiological signals between people with syncope and healthy controls.
Available to supervise honours students.
Honours topics and outlines
Please see PhD topics above: Most PhD topics above can be adapted to suit a Honours student project.
Fields of Research
- Biomechanical Engineering - 400303
- 2019, Swinburne, Travel Award, Swinburne University
- 2013, International, Travel Award, Organization for Computational Neuroscience
- 2006 - 2008: Secretary, IEEE Women in Engineering Student Chapter, The University of Melbourne, Australia
- 2019 - 2022: Committee Member, International Organisation for Computational Neurosciences, United States
- 2019 (current): Associate Editor, IEEE Transactions on Neural Systems and Rehabilitation Engineering, United States
- 2019 - 2022: Committee Member, Swinburne Women Academic Network, Australia
- 2018 - 2022: Committee Member, Swinburne Human Ethics Commitee, Australia
- 2016 (current): Reviewer, IEEE Transactions on Neural Engineering and Rehabilitation, United States
- 2016 (current): Reviewer, Journal of Neural Engineering, United States
- 2015 (current): Reviewer, Journal of Physics in Medicine and Biology , United States
- 2015 (current): Reviewer, International Journal of Control, United States
- 2014 (current): Reviewer, ARC , Australia
- 2010 (current): Reviewer, IEEE EMBC , United States
- 2010 (current): Reviewer, IFAC Symposium on System Identification, United States
- 2010 (current): Reviewer, International Symposium on Bioelectronics and Bioinformatics, United States
- 2016 (current): Reviewer, Students of Brain Research Symposium , Australia
- 2015 (current): Reviewer, Journal of Clinical Ophthalmology, United States
- 2015 (current): Reviewer, • Computational Neuroscience Society conference , United States
- 2017 (current): Reviewer, • Scientific Reports, Nature, United States
- 2018 (current): Reviewer, • Frontiers in Neuroscience, United States
- 2019 (current): Reviewer, European J of Neuroscience , United Kingdom
- 2017 (current): Assessor, NHMRC, Australia
Also published as: Kameneva, Tatiana; Kameneva, T.
This publication listing is provided by Swinburne Research Bank. If you are the owner of this profile, you can update your publications using our online form.
Recent research grants awarded
- 2023: Feasibility study of tVNS as a therapy for early and mid-stage dementia *; Barbara Dicker Brain Science grant
- 2022: Identifying brain pathways regulating sympathetic nervous outflow in human hypertension *; Ideas Grants
- 2022: Retinal implant with closed-loop, multichannel stimulation to improve visual acuity *; NHMRC Development Grant
- 2022: Studying the effects of transcutaneous vagus nerve stimulation (tVNS) using brain imaging in people with major depressive disorder *; Barbara Dicker Brain Science grant
- 2020: Bionic Vision Technologies *; Bionic Vision Technologies Ltd Pty (BVT)
- 2018: 2018 Visiting Fellowships Scheme - Professor Diego Ghezzi *; Swinburne Research, DVCR&D - Internal contributions
- 2018: Targeted Electrical Stimulation Utilising Arbitrary Basis Functions *; ARC Linkage Projects Scheme
- 2017: ARC Training Centre for Personalised Therapeutics Technologies *; ARC Industrial Transformation Training Centres
* Chief Investigator
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