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Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.

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Title: Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.
Authors: Schwarz, Andreas, Escolano, Carlos, Montesano, Luis, Müller-Putz, Gernot R.
Source: Frontiers in Neuroscience; 8/12/2020, Vol. 14, pN.PAG-N.PAG, 17p
Subject Terms: ELECTROENCEPHALOGRAPHY, COLLOIDS, BRAIN-computer interfaces, GOLD standard, WATER
Abstract: Reaching and grasping is an essential part of everybody's life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-Versatile TM system and the dry-electrodes EEG-Hero TM headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets). [ABSTRACT FROM AUTHOR]
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  Data: Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.
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  Data: Reaching and grasping is an essential part of everybody's life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-Versatile<superscript> TM </superscript> system and the dry-electrodes EEG-Hero<superscript> TM </superscript> headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets). [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Frontiers in Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 8/12/2020
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