Med Biol Eng Comput - Compressive sensing scalp EEG signals: implementations and practical performance.

Tópicos

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Resumo

Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.

Resumo Limpo

high miniaturis wearabl comput communic system allow unobtrus conveni long term monitor rang physiolog paramet long term oper physic smallest batteri averag power consumpt wearabl devic must low well known overal power consumpt devic can reduc inclus low power consumpt realtim compress raw physiolog data wearabl devic compress sens new paradigm provid data compress shown signific promis field mri potenti suitabl use wearabl comput system compress process requir wearabl devic low comput complex howev practic perform much depend characterist signal sens util techniqu extrapol one applic anoth long term electroencephalographi eeg fundament tool investig neurolog disord increas use mani nonmed applic braincomput interfac articl investig detail practic perform differ implement compress sens theori appli scalp eeg signal

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