An MCU Implementation of PCA/PSA Streaming Algorithms for EEG Features Extraction

Luciano Prono, Alex Marchioni, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Patient monitoring requires the acquisition of increasingly larger amounts of biosignal data that needs to be managed and transferred with minimum energy consumption. As huge quantities of data are often very redundant, it is possible to reduce their size directly on the edge of the system, right after the acquisition. To do so, subspace analysis can be considered a fundamental tool that can be used to significantly reduce the size of high-dimensional data, thus minimizing the requirements for data transfer. The problem of these methods is that they often come with big memory and computation requirements, as they are ultimately equivalent to the expensive eigenspace evaluation. In order to use subspace analysis methods with the minimum requirements in terms of cost and energy consumption, we here rely on two specialized streaming algorithms for the estimation of the subspace of electroencephalogram (EEG) signals directly after the acquisition on edge devices. The implementation of these state-of-the-art algorithms is tested on a common low-end microcontroller unit (MCU), which is an ideal candidate as edge computing digital hardware platform. The functional performance of these methods is evaluated along with the requirements in term of computational time, energy consumption and memory footprint.
Original languageEnglish (US)
Title of host publicationBioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728172040
StatePublished - Jan 1 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15


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