Remote Neuro-Cognitive Impairment Sensing Based on P300 Spatio-Temporal Monitoring

A novel mobile healthcare solution for remotely monitoring neuro-cognitive efficiency is here presented. The method is based on the spatio-temporal characterization of a specific event-related potential, called P300, induced in our brain by a target stimulus. P300 analysis is used as a biomarker: the amplitude and latency of the signal are quality indexes of the brain activity. Up to now, the P300 characterization has been performed in hospital through EEG analysis and it has not been experimented an algorithm that can work remotely and learn from the subject performance. The proposed m-health service allows remote EEG monitoring of P300 through a “plug and play” system based on the video game reaction of the subject under test. The signal processing is achieved by tuned residue iteration decomposition (t-RIDE). The methodology has been tested on the parietal-cortex area (Pz, Fz, and Cz) of 12 subjects involved in three different cognitive tasks with increasing difficulty. For the set of considered subjects, a P300 deviation has been detected: the amplitude ranges around 2.8-8 μV and latency around 300-410 ms. To demonstrate the improvement achieved by the proposed algorithm respect the state of the art, a comparison between t-RIDE, RIDE, independent component analysis (ICA) approaches, and grand average method is here reported. t-RIDE and ICA analyses report the same results (0.1% deviation) using the same data set (game with a detection of 40 targets). Nevertheless, t-RIDE is 1.6 times faster than ICA since converges in 79 iterations (i.e., t-RIDE: 1.95s against ICA: 3.1s). Furthermore, t-RIDE reaches 80% of accuracy after only 13 targets (task time can be reduced to 65s); differently from ICA, t-RIDE can be performed even on a single channel. The procedure shows fast diagnosis capability in cognitive deficit, including mild and heavy cognitive impairment.