Moving Average Convergence Divergence Filter Preprocessing for Real-Time Event-Related Peak Activity Onset Detection

August 1, 2014
Arnaud Delorme, PhD

Application to fNIRS Signals

Durantin G, Scannella S, Gateau T, Delorme A, Dehais F. (2014) Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals. Conf Proc IEEE Eng Med Biol Soc. :2107-10. doi: 10.1109/EMBC.2014.6944032.


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.

Read the Paper

Join Our Global Community

Receive curated mind-bending, heart-enlivening content. We’ll never share your email address and you can unsubscribe any time.

Back to Top