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EEG Data Analysis for Neurodegeneration

Our EEG biomarkers in combination with Artificial Intelligence can be used for cognitive decline detection, patient stratification in clinical trials and clinical decision support.

We study neurodegenerative diseases through the application of EEG. The main goal is to discover neuropathology at early stages of the disease, where the brain has not been yet seriously affected and cure is still possible.  Early identification and intervention in cognitive impairment facilitate tailored treatment approaches, appropriate support, and more efficacious disease progression monitoring by healthcare professionals. This is one of our more active R+D areas, with several projects in recent years (see below).

Extensive literature have validated the usage of biomarkers characterizing the so-called "slowing" of the EEG, an increase of low-frequency and a decrease of high-frequency EEG content. Moreover EEG connectivity biomarkers are discriminatory of the prodromal phase in different neurodegenerative diseases like Alzheimer's and Parkinsons' Disease.

In the project ParkinsonAID, which has been funded within the Horizon Europe GATEKEEPER project, we have collaborated with Hospital Sant Pau Barcelona to carry out a pilot study to test our Artificial Intelligence (AI) powered Decision Support System (DSS) based on EEG data. We have carried out this pilot with 20 subjects with a PD diagnostic, 10 of them with a diagnostic of mild cognitive impairment, which we have successfully detected. 

With the support of the Michael J Fox Foundation, we have developed a Decision Support System for the early detection of Parkinson’s Disease from the analysis of EEG recordings – 8 years on average before its diagnosis. The developed machine learning algorithms (SVM, Random Forests and Deep Learning) have translational value for the development of drugs and for developing neuroprotective publich health measures.

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