Resilience Aspects in Distributed Wireless Electroencephalographic Sampling

Abstract

Resilience aspects of remote electroencephalography sampling are considered. The possibility to use motion sensors data and measurement of industrial power network interference for detection of failed sampling channels is demonstrated. No significant correlation between signals of failed channels and motion sensors data is shown. Level of 50 Hz spectral component from failed channels significantly differs from level of 50 Hz component of normally operating channel. Conclusions about application of these results for increasing resilience of electroencephalography sampling is made.

Exploiting EEG Signals for Eye Motion Tracking

“…The main artefacts of muscular activity are caused by eyes blinking. Artefacts caused by unstable contacts of the EEG headset electrodes can be detected using the methods described in [28]. We removed the muscular activity artefacts applying the Independent Components Analysis (ICA).…”