Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting more than percent of the global population. It is characterized by motor symptoms such as tremors, rigidity, bradykinesia, and balance issues. Despite advances in understanding and treating PD, there remains a need for new therapeutic approaches. One promising method that has captured our interest is electroencephalogram neurofeedback (EEG NFB). This technique, based on principles of instrumental learning and neuroplasticity, allows individuals to actively influence their brain waves, potentially improving cognitive and motor functions. We are particularly interested in how EEG NFB can alleviate motor deficits, as current studies suggest that specific brain wave frequencies, such as beta and sensorimotor rhythm (SMR), play a crucial role in regulating motor functions.
We conducted a systematic review of the literature exploring the impact of EEG NFB on alleviating motor deficits in PD patients. The aim of our study was to assess existing evidence, identify key trends, and determine potential directions for future research in this area. We reviewed various studies obtained from PubMed, Scopus, Web of Science, and Europe PMC that utilized EEG NFB to address motor symptoms of PD and analyzed the results in the context of potential mechanisms of action, practical implications, and challenges in implementing this method.
The final number of studies meeting our inclusion criteria was five. These studies varied significantly in methodology and protocols, including a case study, a pilot study, and three randomized controlled trials. Despite these differences, results indicated that EEG NFB can improve motor deficits related to balance and mobility issues. However, further research is needed to draw reliable conclusions, as current results are based on small samples and short-term studies.
It is also important to develop more precise protocols and tailor therapies to the individual needs of patients. EEG NFB holds great potential for the future, especially with the integration of machine learning and virtual reality, which could significantly enhance the efficacy and adaptability of the therapy.
|