Profile image for Tatiana Kameneva

Dr Tatiana Kameneva

Senior Lecturer
PhD, The University of Melbourne, Australia; Masters, Kazakh National University, Kazakhstan; Bachelor Degree, Kazakh State University, Kazakhstan


Dr Tatiana Kameneva received PhD from the University of Melbourne in 2008; currently, she is a faculty member at Swinburne University of Technology. Tatiana’s research interests include control theory tools and their applications to life sciences and neuroprosthetic implants.  Tatiana's work contributes to the understanding of neural information processing in response to stimuli. She studies how electrical and optical stimulation affects neural activations and works on the development of new stimulation methods that can be used across a broad range of medical bionics applications.

Research interests

Biophotonics; Biomedical science; Biotechnology

PhD candidate and honours supervision

Higher degrees by research

Accredited to supervise Masters & Doctoral students as Principal Supervisor.

PhD topics and outlines

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.

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. 


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

Teaching areas



  • 2019, Swinburne, Travel Award, Swinburne University
  • 2013, International, Travel Award, Organization for Computational Neuroscience

Professional memberships

  • 2006 - 2008: Secretary, IEEE Women in Engineering Student Chapter, The University of Melbourne, Australia
  • 2019 (current): Committee Member, International Organisation for Computational Neurosciences , United States
  • 2019 (current): Associate Editor, IEEE Transactions on Neural Systems and Rehabilitation Engineering, United States
  • 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

  • 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

Recent media

There are no media items to display