title: Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals.
abstract: 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.
authors.forename: Gautier
authors.initials: G
authors.lastname: Durantin
authors.forename: Sebastien
authors.initials: S
authors.lastname: Scannella
authors.forename: Thibault
authors.initials: T
authors.lastname: Gateau
authors.forename: Arnaud
authors.initials: A
authors.lastname: Delorme
authors.forename: Frederic
authors.initials: F
authors.lastname: Dehais
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D003199:Computer Systems; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013997:Time Factors; D055815:Young Adult