Brain-computer interface using P300: a gaming approach for neurocognitive impairment diagnosis

This paper proposes a novel mobile healthcare system for remotely monitoring neuro-cognitive functions of impaired subjects and proposing possible treatments. Currently, only hospital centers perform similar analyses through fixed and wired electroencephalography (EEG) inspection. The solution proposed here works wirelessly and improves its accuracy learning by performances of the subject playing a game/test. The system is based on spatio-temporal detection and characterization of a specific brain potential named P300. It includes: i) a wearable wireless EEG device; ii) a gateway (tablet or smartphone) processing gathered data, also providing the test/game to the user. Given the above hardware settings, a new algorithm, named tuned-Residue Iteration Decomposition (t-RIDE), provides spatiotemporal features of P300s and a semantic-based reasoner allows taking into account factors which could modify the test if performed in non-standard conditions. The system has been adopted with 12 subjects involved in three different cognitive tasks with increasing difficulty. Fast diagnosis of cognitive deficits is reached, including mild and heavy impairments cases: t-RIDE processing is performed in 1.95s (after 79 iterations for convergence) whereas semantic matchmaking routine requires 2.5ms in the worst case. A case study for an Alzheimer injured patient is reported to corroborate and clarify the proposed approach.