Use of Electroencephalography-based Neurofeedback in People with Parkinson's Disease


  • Laura Blaznik University of Ljubljana
  • Uroš Marušič Science and Research Centre Koper



Parkinson’s disease (PD) is the second most common neurodegenerative disease and one of the leading causes of disability. Around 10 million people have this condition worldwide [1]. It is a progressive disease characterized by gradually worsening symptoms. Individuals with PD develop different motor and non-motor symptoms. Some of the non-motor symptoms can appear years before a person is diagnosed with PD. Each year, PD diagnoses increase by 1 %  [1]. Most commonly, the motor symptoms are treated using levodopa, which is absorbed by the brain cells and transformed into dopamine, a neurotransmitter that controls movement [1]. However, there are currently no other effective drugs available to treat PD. An alternative non-invasive and non-pharmaceutical technique is neurofeedback (NFB), a type of biofeedback that measures neuron activity. We know different NFB methods that work according to different principles.

In my dissertation, I will focus on electroencephalography-based NFB (EEG NFB), which has a high temporal resolution and provides real-time feedback to participants [2]. It is also cheaper and easier to transport compared to other systems. Patients are shown the feedback that they can use to change their brain waves according to the cues given by an expert  [2]. Such training is known as neurofeedback training (NFT).

Aims and Methods

The aim of my master's thesis is to systematically review experimental articles in which NFT was used as a method to treat patients with PD.

Additionally, I aim to check experimental articles where the NFT was used to improve the motor skills of healthy participants. One of the main goals of the thesis is also to determine the possibility of implementing EEG NFT in clinics or everyday life, without the need for the presence of an expert or clinician. With this, the ecological validity of the NFT method can be improved.

For a systematic review, I will follow the PRISMA-S strategy [3]. Articles will be collected from PubMed, Google Scholar, and Frontiers web pages.

Expected Results

I expect, to find EEG NFT leading to improvements in motor deficits in PB patients. Additionally, I expect to develop a protocol for the application of the EEG NFT that can be utilized without the need for clinician supervision.


[1] M. J. Armstrong and M. S. Okun, “Diagnosis and treatment of parkinson disease,” JAMA, vol. 323, no. 6, p. 548, 2020. doi:10.1001/jama.2019.22360

[2] R. Sitaram et al., “Author correction: Closed-loop brain training: The science of neurofeedback,” Nature Reviews Neuroscience, vol. 20, no. 5, pp. 314–314, 2019. doi:10.1038/s41583-019-0161-1

[3] M. L. Rethlefsen et al., “Prisma-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews,” BioMed Central, 2019. doi:10.31219/


Author Biography

  • Uroš Marušič, Science and Research Centre Koper

    Institute for Kinesiology Research