Version 1
: Received: 27 October 2018 / Approved: 30 October 2018 / Online: 30 October 2018 (09:22:56 CET)
How to cite:
Qaisar, S.M.; Ramadan, R.; Subasi, A. An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals. Preprints2018, 2018100720. https://doi.org/10.20944/preprints201810.0720.v1
Qaisar, S.M.; Ramadan, R.; Subasi, A. An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals. Preprints 2018, 2018100720. https://doi.org/10.20944/preprints201810.0720.v1
Qaisar, S.M.; Ramadan, R.; Subasi, A. An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals. Preprints2018, 2018100720. https://doi.org/10.20944/preprints201810.0720.v1
APA Style
Qaisar, S.M., Ramadan, R., & Subasi, A. (2018). An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals. Preprints. https://doi.org/10.20944/preprints201810.0720.v1
Chicago/Turabian Style
Qaisar, S.M., R. Ramadan and A. Subasi. 2018 "An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals" Preprints. https://doi.org/10.20944/preprints201810.0720.v1
Abstract
The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.
Keywords
electroencephalogram; event-driven signal acquisition; activity selection; data compression; adaptive rate filtering
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
