EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Jochumsen, Mads; Knoche, Hendrik; Kjær, Troels W.; Dinesen, Birthe; Kidmose, Preben

doi:10.3390/s20102804

Cited by 15 publications

(13 citation statements)

References 54 publications

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“…Studies have evaluated usability and performance of emerging mobile systems and compared them to laboratory systems considered “gold standard” ( Guger et al, 2012 ; Pinegger et al, 2016 ; Di Flumeri et al, 2019 ). Recently, Jochumsen et al (2020a , b) evaluated not only the performance of several mobile EEG systems with respect to movement intention detection from the LFTD, but also evaluated their usability with regards to patients, relatives and therapists. Nevertheless, datasets eligible for quantifying different electrode sets are rather scarce.…”

Section: Introductionmentioning

confidence: 99%

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

Schwarz

Escolano²,

Montesano

et al. 2020

Front. Neurosci.

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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 ).

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Section: Introductionmentioning

confidence: 99%

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

Schwarz

Escolano²,

Montesano

et al. 2020

Front. Neurosci.

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“…In recent years, several low-cost commercial EEG systems have become available [ 18 ]. Some of these systems may not be useful for applications where neural plasticity is induced in the motor system, since they do not record electrical activity from the relevant brain areas [ 19 ]. However, it is possible to record the electrical activity of the motor cortex with some low-cost EEG systems.…”

Section: Introductionmentioning

confidence: 99%

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Jochumsen

Janjua

Arceo

et al. 2021

Sensors

Self Cite

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Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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“…Such preparation process means that only trained researchers or clinicians can apply the system. It cannot be used independently by participants or patients at home which could be beneficial especially for clinical research and observation of neurological patients (Hinrichs et al, 2020;Jochumsen, Knoche, Kjaer, Dinesen, & Kidmose, 2020;Radüntz, 2018). The lengthy preparation process and skin abrasion are also not suitable for participants with sensory sensitivities, attention difficulties and restlessness which are often observed in neurodevelopmental conditions such as autism spectrum disorder (ASD) or attention deficit hyperactivity disorder (ASD) and may be problematic for young children.…”

Section: Introductionmentioning

confidence: 99%

“…No metal parts of the electrodes come into direct contact with the skin, there is no need for skin preparation or washing hair and the procedure is generally less time consuming in comparison to the gel-based electrode systems. The quality of EEG recordings using waterbased electrodes have only been evaluated in the context of brain computer interface (BCI) designs (Jochumsen et al, 2020;Pinegger et al, 2016) and the results are promising. The noise levels during a short circuit recording expressed as the root mean square values were the lowest in a water-based compared to gel-based and dry electrode systems (Pinegger et al, 2016) and the signal to noise ratio was comparable between water-and gel-based systems (Jochumsen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

In search for the most optimal EEG method: A practical evaluation of a water-based electrode EEG system

Topor

Opitz

Dean

2021

Preprint

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The study assessed a new mobile electroencephalography (EEG) system with water-based electrodes for its applicability in time-frequency and event related potential research. It was compared to a standard gel-based wired system. EEG was recorded on two occasions as participants completed the flanker task, first with the gel-based system followed by the water-based system. Technical and practical considerations for the application of the new water-based system are reported based on the participant and experimenter experiences. Empirical comparisons focused on EEG data noise levels, frequency power across four bands including theta, alpha, low beta and high beta and P300 and ERN event related potential components. The water-based system registered more noise compared to the gel-based system which resulted in increased loss of data during artefact rejection. Signal to noise ratio was significantly lower for the water-based system in the parietal channels which impacted the observed parietal beta power. It also led to a shift in topography of the maximal P300 activity from parietal to frontal regions. It is also evident, that the water-based system may be prone to slow drift noise which may affect the reliability and consistency of low frequency band analyses. Considerations for the use of this specific system for time-frequency and event related potentials are discussed.

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EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Cited by 15 publications

References 54 publications

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

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

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

In search for the most optimal EEG method: A practical evaluation of a water-based electrode EEG system

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