Object: Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research.
Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic.
Results: Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG.
Conclusions: We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research.
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http://dx.doi.org/10.1007/s10072-023-06981-9 | DOI Listing |
J Neural Eng
January 2025
Department of Biomedical Engineering, The University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA.
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair.
View Article and Find Full Text PDFJ Neural Eng
January 2025
ECE & Neurology, University of Texas at Austin, 301 E. Dean Keeton St. C2100, Austin, Texas, 78712-1139, UNITED STATES.
Objective: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).
View Article and Find Full Text PDFElife
January 2025
Department of Neurology, TUM School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany.
Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers.
View Article and Find Full Text PDFElectroencephalographic (EEG) recordings in individuals with Fragile X Syndrome (FXS) and the mouse model of FXS ( KO) display cortical hyperexcitability at rest, as well as deficits in sensory-driven cortical network synchrony. A form of circuit hyperexcitability is observed in cortical slices of KO mice as prolonged persistent activity, or Up, states. It is unknown if the circuit mechanisms that cause prolonged Up states contribute to FXS-relevant EEG phenotypes.
View Article and Find Full Text PDFNat Ment Health
January 2025
Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland.
Atypical face processing is commonly reported in autism. Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how variation in brain anatomy and function jointly impacts face processing and social functioning.
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