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Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2020-08-12 , DOI: 10.3389/fnins.2020.00849
Andreas Schwarz 1 , Carlos Escolano 2 , Luis Montesano 2, 3 , Gernot R Müller-Putz 1, 4
Affiliation  

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-VersatileTM system and the dry-electrodes EEG-HeroTM 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).

中文翻译:

使用凝胶、水和干脑电图系统分析和解码自然的触及和抓握动作

伸手抓住是每个人生活中必不可少的一部分,它允许与环境进行有意义的互动,是独立生活方式的关键。最近基于脑电图 (EEG) 的研究已经表明,可以在 EEG 中识别自然伸手动作的神经相关性。然而,在实验室环境中获得的这些结果是否可以过渡到家庭使用的移动适用 EEG 系统仍然是一个问题。在当前的研究中,我们调查了是否可以使用移动 EEG 系统(即水基 EEG-VersatileTM 系统和干电极 EEG-HeroTM 耳机)成功识别和解码基于 EEG 的自然伸手动作相关性。此外,我们还分析了在实验室环境中获得的基于凝胶的录音(g.USBamp / g.Ladybird,黄金标准),遵循相同的实验参数。对于每个记录系统,15 名研究参与者对玻璃杯(手掌抓握)和勺子(横向抓握)进行了 80 次自发的伸手抓握动作。我们的结果证实,使用这些移动系统可以成功识别基于 EEG 的触及和抓取动作的相关性。在结合了运动条件和休息的单次试验多类解码方法中,我们可以证明低频时域 (LFTD) 相关性也是可解码的。根据看不见的测试数据计算得出的大平均峰值准确度,水基电极系统为 62.3% (9.2% STD),而干电极耳机达到 56.4% (8% STD)。对于基于凝胶的电极系统,可以实现 61.3% (8.6% STD)。
更新日期:2020-08-12
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