Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ortega:2020:10.3389/fnins.2020.00919,
author = {Ortega, San Miguel P and Zhao, T and Faisal, AA},
doi = {10.3389/fnins.2020.00919},
journal = {Frontiers in Neuroscience},
pages = {1--10},
title = {HYGRIP: Full-stack characterisation of neurobehavioural signals (fNIRS, EEG, EMG, force and breathing) during a bimanual grip force control task},
url = {http://dx.doi.org/10.3389/fnins.2020.00919},
volume = {14},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Brain-computer interfaces (BCIs) have achieved important milestones in recent years, but the major number of breakthroughs in the continuous control of movement have focused on invasive neural interfaces with motor cortex or peripheral nerves. In contrast, non-invasive BCIs have made primarily progress in continuous decoding using event-related data, while the direct decoding of movement command or muscle force from brain data is an open challenge.Multi-modal signals from human cortex, obtained from mobile brain imaging that combines oxygenation and electrical neuronal signals, do not yet exploit their full potential due to the lack of computational techniques able to fuse and decode these hybrid measurements.To stimulate the research community and machine learning techniques closer to the state-of-the-art in artificial intelligence we release herewith a holistic data set of hybrid non-invasive measures for continuous force decoding: the Hybrid Dynamic Grip (HYGRIP) data set. We aim to provide a complete data set, that comprises the target force for the left/right hand, cortical brain signals in form of electroencephalography (EEG) with high temporal resolution and functional near-infrared spectroscopy (fNIRS) that captures in higher spatial resolution a BOLD-like cortical brain response, as well as the muscle activity (EMG) of the grip muscles, the force generated at the grip sensor (force), as well as confounding noise sources, such as breathing and eye movement activity during the task.In total, 14 right-handed subjects performed a uni-manual dynamic grip force task within $25-50\%$ of each hand's maximum voluntary contraction. HYGRIP is intended as a benchmark with two open challenges and research questions for grip-force decoding.First, the exploitation and fusion of data from brain signals spanning very different time-scales, as EEG changes about three orders of magnitude faster than fNIRS.Second, the decoding of whole-brain signals associated with the use of
AU  - Ortega,San Miguel P
AU  - Zhao,T
AU  - Faisal,AA
DO  - 10.3389/fnins.2020.00919
EP  - 10
PY  - 2020///
SN  - 1662-453X
SP  - 1
TI  - HYGRIP: Full-stack characterisation of neurobehavioural signals (fNIRS, EEG, EMG, force and breathing) during a bimanual grip force control task
T2  - Frontiers in Neuroscience
UR  - http://dx.doi.org/10.3389/fnins.2020.00919
UR  - https://www.frontiersin.org/articles/10.3389/fnins.2020.00919/full
UR  - http://hdl.handle.net/10044/1/82249
VL  - 14
ER  -
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