Multisource biometric data standardization and homogenization for prevention and treatment of neurological pathologies and disorders

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Short description of project idea : 
The project aims to develop a digital solution that prevents, minimizes or mitigates the consequences of neurological pathologies or disorders. The main objective is the creation of a multisource data-framework that allows the generation of neuronal behavior patterns characterizing symptomatic of pathologies such as Alzheimer's or Parkinson's or learning disorders such as Dyslexia, TDHA or Autism. The set of data acquisition devices will consist of different wearables that monitor biometric variables and a wireless module for setting-up electroencephalograms. On the other hand, cognitive experimentation will be performed through transcranial stimulation (tDCS, tACS and tRNS) and a virtual reality platform.
Main objectives of the project and how will they be achieved: 
Currently, biometric data that allow the diagnosis and treatment of neurological pathologies and disorders are extracted and analyzed in isolated environments. The main objective of the project is the creation of a standard and interoperable environment that allows the analysis of heterogenous biometric data. For this, the data, both analog and digital, must be analyzed and homogenized in a format that allows the usage of analytics tools to generate specific patterns for each scenario.
Challenges that may determine the impact of the project: 
Due to the multitude of data sources that can enrich the diagnosis, one of the main challenges will be the integration of the largest amount of data for later analysis. To do this, the selected data sources must be identified, acquired, filtered and secured in order to have the largest volume of data and sampling maximization. Integration into efficient and robust databases will be vital for high-performance distributed computing. Once the storage and processing environment is achieved, the key factor will be the development of algorithms that enable the deep learning of the pathologies or disorders selected. The participation of stakeholders such as patients or caregivers, researchers/scientists, specialized doctors or healthcare companies in open innovation processes is another challenge to generate a common knowledge framework. The empowerment of the patient is presented as a differential factor looking for the generation of data frames that feed the analysis platform. Both clinical trials and cognitive stimulation processes must be co-designed and co-participated by multidisciplinary agents in order to maximize impact.
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Data Long 16

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