TY - GEN
T1 - Towards a Unified Framework for De-noising Neural Signals
AU - Kilicarslan, Atilla
AU - Contreras-Vidal, Jose L.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Neural signals provide key information for decision-making processes in multiple disciplines including medicine, engineering, and neuroscience. The correct interpretation of these signals, however, requires substantial processing, especially when the signals exhibit low Signal to Noise Ratio (SNR). Electroencephalographic (EEG) signals are considered among this group and require effective handling of multiple types of artifactual components. Unfortunately, most available de-noising tools are suitable only for offline signal processing. For some artifacts (e.g., EEG motion artifacts), no established method of effective denoising exists for offline or real-time applications. Thus, there is a critical need for methods that can handle artifacts in neural signals with high performance, reliability and real-time capability. Here, we propose novel methods for handling some of the most challenging artifacts that exhibit highly complex dynamics, including motion artifacts. Having the same core sample-adaptive processing tool used for handling different types of artifacts, we present our efforts towards a unified framework for neural data artifact denoising with real-time compatibility.
AB - Neural signals provide key information for decision-making processes in multiple disciplines including medicine, engineering, and neuroscience. The correct interpretation of these signals, however, requires substantial processing, especially when the signals exhibit low Signal to Noise Ratio (SNR). Electroencephalographic (EEG) signals are considered among this group and require effective handling of multiple types of artifactual components. Unfortunately, most available de-noising tools are suitable only for offline signal processing. For some artifacts (e.g., EEG motion artifacts), no established method of effective denoising exists for offline or real-time applications. Thus, there is a critical need for methods that can handle artifacts in neural signals with high performance, reliability and real-time capability. Here, we propose novel methods for handling some of the most challenging artifacts that exhibit highly complex dynamics, including motion artifacts. Having the same core sample-adaptive processing tool used for handling different types of artifacts, we present our efforts towards a unified framework for neural data artifact denoising with real-time compatibility.
UR - http://www.scopus.com/inward/record.url?scp=85077865989&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077865989&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8856876
DO - 10.1109/EMBC.2019.8856876
M3 - Conference contribution
C2 - 31945974
AN - SCOPUS:85077865989
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 620
EP - 623
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -