Projects:2019s1-155 Brain Computer Interface Control for Biomedical Applications
Project Team
Students
Zhiying Lin
Kayla Wahlstrom
Supervisors
Associate Professor Mathias Baumert
Mr David Bowler
Introduction
Stroke is one of the leading causes of disability in Australia [3], with 65% of stroke survivors presenting persistent disability following the initial cerebrovascular episode. In addition, motor impairments are the most prevalent post-stroke disability [2]. This decrease in movement coordination and control means the patient’s ability to perform daily tasks unassisted is impaired [1].
Stroke occurs when blood supply to the brain is disrupted, depriving the brain cells of oxygen and vital nutrients. Brain cells not receiving oxygen and these vital nutrients will die, causing damage to the brain known as a cerebral infarct (necrosis, or tissue death) [1]. Tissue death in the motor regions of the brain impairs the brain's ability to execute movement.
Methods such as exercise and training have predominantly been used as stroke rehabilitative strategies to restore motor function, however, training approaches to improve the effects of these current methods are being investigated and developed for post-stroke disability [2]. Fundamental to this investigation is the knowledge of neuroplasticity and the intrinsic behaviour of the brain and its motor regions following stroke.
This project will investigate a non-invasive brain-computer interface (BCI) to provide users with the ability to control motor movement via an external limb, and with long-term use, aim to promote neuroplasticity to improve or restore motor movement in post-stroke patients.
Motivation
Previous UofA student Work
Work completed by previous students included designing a flexible headset using an elastic strap to hold the electrodes. BCI software was also developed, but due to time constraints, some features such as classifiers and the function of Data Tab on the BCI framework were not fully implemented. Also developed was a new mechanical 3D glove, and testing of the system was completed using third-party software platform OpenViBE.
Objective
Background
What is a BCI
The brain-computer interface (BCI) is a collaboration between the brain and a device that allows signals from the brain to direct some external activity, such as controlling a cursor or a prosthetic limb. This interface enables direct communication between the brain and the controlled object.
Types of BCIs
There are many different techniques to measure brain signals. These can divided into non-invasive, semi-invasive and invasive. [1]
The Brain and Neural Oscillations
Neuroimaging approaches in BCI[2]
1. Electroencephalography (EEG) measures the difference in potential on the scalp due to neural activity, which is the sum of thousands or millions of cortical neurons' postsynaptic excitatory potential and inhibitory potential.
2. Magnetoencephalography (MEG) measures magnetic field differences related to neuron activity.
3. Functional Magnetic Resonance Imaging (fMRI) was used to detect changes in local cerebral blood volume, cerebral blood flow and oxygenation level during neuron activation.
4. Near Infrared Spectroscopy (NIRS) USES the characteristics of light in the near infrared spectrum to penetrate the skull to a considerable depth for the study of brain metabolism. It can detect the change of hemoglobin concentration in the process of local nerve activity in different wavelengths of weak light intensity.
References
1. http://learn.neurotechedu.com/introtobci/
2. Byoung-Kyong Min, Matthew J. Marzelli and Seung-Schik Yoo (2010) Neuroimaging-based approaches in the brain–computer interface, Available at: https://www.researchgate.net/publication/46109898 (Accessed: 12/4/2019).
3. Strokefoundation.org.au. (2019). About Stroke. [online] Available at: https://strokefoundation.org.au/About-Stroke [Accessed 14 Apr. 2019].
4. Dimyan, M. and Cohen, L. (2019). Neuroplasticity in the context of motor rehabilitation after stroke. [online] Available at: https://www.nature.com/articles/nrneurol.2010.200 [Accessed 14 Apr. 2019].
