Resilience Aspects in Distributed Wireless Electroencephalographic Sampling
This addresses reliability issues in EEG monitoring for medical or research applications, but it is incremental as it builds on existing detection methods.
The paper tackled the problem of detecting failed channels in remote electroencephalography (EEG) sampling by analyzing motion sensor data and industrial power network interference. The result showed no correlation with motion sensors but a significant difference in 50 Hz spectral components between failed and normal channels.
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.