Resilience Aspects in Distributed Wireless Electroencephalographic Sampling

TL;DR

This paper explores methods to improve the resilience of remote EEG sampling by detecting failed channels using motion sensors and power interference analysis, highlighting spectral differences in failure detection.

Contribution

It introduces novel techniques for identifying failed EEG channels through spectral analysis and sensor data, enhancing remote EEG system robustness.

Findings

Failed channels show distinct 50 Hz spectral signatures.

Motion sensors do not correlate with channel failures.

Power interference analysis can detect sampling failures.

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.