Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals

Kobler, Reinmar J.; Sburlea, Andreea Ioana; Lopes-Dias, Catarina; Schwarz, Andreas; Hirata, Masayuki; Müller-Putz, Gernot

doi:10.1016/j.neuroimage.2020.117000

Cited by 36 publications

(43 citation statements)

References 38 publications

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“…After the main runs, two runs of continuous rest were recorded, with a duration of one minute each. Finally, participants were led through two runs of controlled eye artifacts, i.e., saccades and blinks (Kobler et al, 2020b).…”

Section: Methodsmentioning

confidence: 99%

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Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance

Hehenberger

Batistić

Sburlea

et al. 2021

Front. Hum. Neurosci.

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Motor imagery is a popular technique employed as a motor rehabilitation tool, or to control assistive devices to substitute lost motor function. In both said areas of application, artificial somatosensory input helps to mirror the sensorimotor loop by providing kinesthetic feedback or guidance in a more intuitive fashion than via visual input. In this work, we study directional and movement-related information in electroencephalographic signals acquired during a visually guided center-out motor imagery task in two conditions, i.e., with and without additional somatosensory input in the form of vibrotactile guidance. Imagined movements to the right and forward could be discriminated in low-frequency electroencephalographic amplitudes with group level peak accuracies of 70% with vibrotactile guidance, and 67% without vibrotactile guidance. The peak accuracies with and without vibrotactile guidance were not significantly different. Furthermore, the motor imagery could be classified against a resting baseline with group level accuracies between 76 and 83%, using either low-frequency amplitude features or μ and β power spectral features. On average, accuracies were higher with vibrotactile guidance, while this difference was only significant in the latter set of features. Our findings suggest that directional information in low-frequency electroencephalographic amplitudes is retained in the presence of vibrotactile guidance. Moreover, they hint at an enhancing effect on motor-related μ and β spectral features when vibrotactile guidance is provided.

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Section: Methodsmentioning

confidence: 99%

“…On average, 20 trials per subject were rejected. Additionally, we employed the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm (Kobler et al, 2020b) in order to attenuate eye movements correlated with the task. The SGEYESUB model was trained on the two runs of controlled eye artifacts.…”

Section: Preprocessingmentioning

confidence: 99%

Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance

Hehenberger

Batistić

Sburlea

et al. 2021

Front. Hum. Neurosci.

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“…We recently proposed a short paradigm to record approximately 5 min of data during specific eye movements (Kobler et al, 2017 ) and have used it in our recent offline (Kobler et al, 2018 ; Schwarz et al, 2020 ; Kobler et al, 2020a , c ) and online (Martínez-Cagigal et al, 2020 ; Mondini et al, 2020 ; Pereira et al, 2021 ) studies. In Kobler et al ( 2020b ), we proposed a new algorithm denoted sparse generalized eye artifact subspace subtraction (SGEYESUB). Using M/EEG data of 69 participants, we found that SGEYESUB achieved state-of-the-art eye artifact correction and at the same time maintained resting brain activity as well as MRCPs and ErrPs.…”

Section: Methodsmentioning

confidence: 99%

Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control

Müller-Putz

Kobler

Pereira

et al. 2022

Front. Hum. Neurosci.

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Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the “Feel Your Reach” framework to people with cervical spinal cord injury and evaluate the decoders’ performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.

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“…Before the experiment, we recorded one block in which the participant performed eye movements [48,49]. The experiment then consisted of 8 blocks of 30 trials each.…”

Section: Experiments Overviewmentioning

confidence: 99%

“…Eye movements and blinks were removed online from the EEG data, using the subspace subtraction algorithm [48,49] and the eye movement data recorded right before the start of the experiment. For the online detection of ErrPs with the generic classifier, the EEG data were bandpass filtered between 1 and 10 Hz with a causal Butterworth filter of order 4.…”

Section: Data Processingmentioning

confidence: 99%

Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier

Lopes-Dias,

Sburlea,

Breitegger

et al. 2020

Preprint

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A brain-computer interface (BCI) user awareness of an error is associated with a cortical signature named error-related potential (ErrP). The incorporation of ErrPs' detection in BCIs can improve BCIs' performance.Objective: This work is three-folded. First, we investigate if an ErrP classifier is transferable from able-bodied participants to participants with spinal cord injury (SCI). Second, we test this generic ErrP classifier with SCI and control participants, in an online experiment without offline calibration. Third, we investigate the morphology of ErrPs in both groups of participants.Approach: We used previously recorded electroencephalographic (EEG) data from able-bodied participants to train an ErrP classifier. We tested the classifier asynchronously, in an online experiment with 16 new participants: 8 participants with SCI and 8 able-bodied control participants. The experiment had no offline calibration and participants received feedback regarding the ErrPs' detection from its start. For a matter of fluidity of the experiment, the feedback regarding false positive ErrP detections was not presented to the participants but these detections were taken into account in the evaluation of the classifier. The generic classifier was not trained with the user's brain signals. Still, its performance was optimized during the online experiment with the use of personalized decision thresholds. The classifier's performance was evaluated using trial-based metrics, which consider the asynchronous detection of ErrPs during the entire trials' duration.Main results: Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed above chance level in participants with clear ErrP signals, independently of the SCI (11 out of 16 participants). Three out of the five participants that obtained chance level results with the generic classifier would have not benefited from the use of a personalized classifier.Significance: This work shows the feasibility of transferring an ErrP classifier from able-bodied participants to participants with SCI, for asynchronous detection of ErrPs in an online experiment without offline calibration, which provided immediate feedback to the users.

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Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals

Cited by 36 publications

References 38 publications

Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance

Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance

Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control

Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier

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