EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5–6 Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1 Hz) with a power budget of 56 mW.

Montagna, F., Buiatti, M., Benatti, S., Rossi, D., Farella, E., Benini, L. (2017). A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems. METHODS, 129, 96-107 [10.1016/j.ymeth.2017.06.019].

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems

MONTAGNA, FABIO
;
BENATTI, SIMONE;ROSSI, DAVIDE;FARELLA, ELISABETTA;BENINI, LUCA
2017

Abstract

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5–6 Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1 Hz) with a power budget of 56 mW.
2017
Montagna, F., Buiatti, M., Benatti, S., Rossi, D., Farella, E., Benini, L. (2017). A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems. METHODS, 129, 96-107 [10.1016/j.ymeth.2017.06.019].
Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca
File in questo prodotto:
File Dimensione Formato  
Methods2107.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 2.61 MB
Formato Adobe PDF
2.61 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/609279
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 7
social impact