Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
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Publications

Citation

BibTex format

@inproceedings{Del:2021:10.1109/EMBC46164.2021.9629672,
author = {Del, Angel Arrieta F and Rojas, Cisneros M and Rivas, JJ and Castrejon, LR and Sucar, LE and Andreu-Perez, J and Orihuela-Espina, F},
doi = {10.1109/EMBC46164.2021.9629672},
pages = {1288--1291},
publisher = {IEEE},
title = {Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform.},
url = {http://dx.doi.org/10.1109/EMBC46164.2021.9629672},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation.
AU  - Del,Angel Arrieta F
AU  - Rojas,Cisneros M
AU  - Rivas,JJ
AU  - Castrejon,LR
AU  - Sucar,LE
AU  - Andreu-Perez,J
AU  - Orihuela-Espina,F
DO  - 10.1109/EMBC46164.2021.9629672
EP  - 1291
PB  - IEEE
PY  - 2021///
SN  - 1557-170X
SP  - 1288
TI  - Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform.
UR  - http://dx.doi.org/10.1109/EMBC46164.2021.9629672
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34891521
UR  - https://ieeexplore.ieee.org/document/9629672
UR  - http://hdl.handle.net/10044/1/96204
ER  -
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