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

Hehenberger, Lea; Batistić, Luka; Sburlea, Andreea Ioana; Müller-Putz, Gernot

doi:10.3389/fnhum.2021.687252

Cited by 8 publications

(29 citation statements)

References 61 publications

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“…Furthermore, the impact of the different neural patterns on the response time and the duration of the detected state was investigated. The results obtained in this respect confirm the investigations carried out by Waldert et al [10] and Hehenberger at al [17], according to which the direction of an intended movement is essentially encoded by signal components lying in the low-frequency signal range. One explanation for the poor state duration value (SCP-Crop1, tstate70 ~ 150 ms) when using the low-frequency signal components could be that the information is transmitted phase-modulated.…”

Section: Discussionsupporting

confidence: 89%

Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application

Strahnen

Kessler

2022

IEEE Access

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The task of a deep neural network (DNN) as a component of a brain-computer interface (BCI) is to analyze the measured EEG data and to recognize the neural signal patterns characteristic of a given motor movement. Our studies are intended to investigate the use of such a DNN in continuous control applications where the EEG signals need to be interpreted continuously, as well as to gain insights into the learned neural patterns. Method: We examined EEGNet, a commonly referenced Convolutional Neural Net (CNN) trained with trials from the BCI Competition IV 2a EEG data set. In addition to the impact of the size and temporal location of the trials on classification accuracy, we examined the contributions and temporal behavior of known neural patterns and their impact on system's response time and the amount of time for which the mental state was detected. Results: Because the optimal temporal positions of the trials are different for the neural patterns involved, we introduced cropped training in which the DNN is trained using trials with different temporal positions. This enabled the DNN to learn the neural patterns in the 0-8 Hz frequency range that are important for a short response time and the patterns in the 8-30 Hz frequency range that are important for determining state duration. Conclusion: Cropped training is essential for achieving a good response time, which could be improved by ~200 ms, as well as for a good state duration, which could be increased from 150 ms to 1.75 s.

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Section: Discussionsupporting

confidence: 89%

Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application

Strahnen

Kessler

2022

IEEE Access

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“…Subsequently, the actuator representing the starting position was turned on, and the hand cue appeared. After a delay of two seconds, both the hand and the vibrotactile input started moving, guiding the participant’s imagined movement adapted from Hehenberger et al ( 2021 ). (E) Classification accuracies for classification between conditions and between directions are represented as grand averages and confidence intervals, as well as MI vs. baseline represented as box plots of the grand average accuracies.…”

Section: Methodsmentioning

confidence: 99%

“…(E) Classification accuracies for classification between conditions and between directions are represented as grand averages and confidence intervals, as well as MI vs. baseline represented as box plots of the grand average accuracies. Purple dashed vertical lines mark the cue movement onset, and blue dash-dotted horizontal lines the threshold of significantly better-than-chance performance [adapted from Hehenberger et al ( 2021 )] (F) Potentials are represented as grand averages and confidence intervals, along with topographic representations at the time point of the grand-average MRCP peak [adapted from Hehenberger et al ( 2021 )].…”

Section: Methodsmentioning

confidence: 99%

“…This issue was taken into account in the design of a follow-up study (Hehenberger et al, 2021 ), where we replaced the executed movement with motor imagery, and directly cued the initiation of the imagined center-out movement. This study included two conditions—MI with concurrent visual and vibrotactile guidance (condition MI+VG), and MI with visual guidance only (condition MI).…”

Section: Methodsmentioning

confidence: 99%

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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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“…processes cannot be viewed as decoupled. Rather, movement actions are adjusted and refined during the execution based on sensory inputs that have a beneficial effect on the BCI control performance [ 20 , 21 ]. One such sensory input is the vibrotactile guidance, which is used (in addition to visual guidance) in one of the two datasets analyzed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Detection of motor imagery based on short-term entropy of time–frequency representations

2023

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Background Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only. Results When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation. Conclusions Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

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

Cited by 8 publications

References 61 publications

Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application

Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application

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

Detection of motor imagery based on short-term entropy of time–frequency representations

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