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The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering, and in-cloud distribution to specialized medical personnel. As such, it presents tradeoffs typical of other cyber-physical systems, where hardware, algorithms, and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects (\n            <jats:italic>n<\/jats:italic>\n            = 12 healthy,\n            <jats:italic>n<\/jats:italic>\n            = 3 mildly cognitive impaired, and\n            <jats:italic>n<\/jats:italic>\n            = 2 with Alzheimer's disease involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s), yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.\n          <\/jats:p>","DOI":"10.1145\/3140236","type":"journal-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T13:02:15Z","timestamp":1532350935000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Mobile Health System for Neurocognitive Impairment Evaluation Based on P300 Detection"],"prefix":"10.1145","volume":"2","author":[{"given":"D. De","family":"Venuto","sequence":"first","affiliation":[{"name":"Dept. of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. 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