Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection: application to fNIRS signals

Durantin, Gautier, Scannella, Sebastien, Gateau, ,Thibault, Delorme, Arnaud and Dehais, Frederic (2014). Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection: application to fNIRS signals. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, United States, (2107-2110). 26-30 August 2014. doi:10.1109/EMBC.2014.6944032


Author Durantin, Gautier
Scannella, Sebastien
Gateau, ,Thibault
Delorme, Arnaud
Dehais, Frederic
Title of paper Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection: application to fNIRS signals
Conference name 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Conference location Chicago, IL, United States
Conference dates 26-30 August 2014
Convener IEEE
Proceedings title 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014   Check publisher's open access policy
Series IEEE Engineering in Medicine and Biology Society Conference Proceedings
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/EMBC.2014.6944032
Open Access Status Not Open Access
ISBN 9781424479290
ISSN 1557-170X
Start page 2107
End page 2110
Total pages 4
Abstract/Summary Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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