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

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.

Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant.

Objective. In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement.Approach. Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation-only condition, and once while simultaneously attempting movement.Main results. We observed mean correlations well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. Additionally, no global improvement over three sessions within five days, both in sensor and in source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found.Significance. No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.

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

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 ß 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 ß spectral features when vibrotactile guidance is provided.

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

For brain-computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance. OBJECTIVE: This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodied participants to participants with a 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 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 ErrP detections from the start. To increase the fluidity of the experiment, 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. However, its performance was optimized during the online experiment by the use of personalized decision thresholds. The classifier's performance was evaluated using trial-based metrics, which considered the asynchronous detection of ErrPs during the entire trial's 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 better than chance 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 benefitted 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.

Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery.

The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.

Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy.

OBJECTIVE: One of the main goals in brain-computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. APPROACH: In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. MAIN RESULTS: At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. SIGNIFICANCE: We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.

A Generic Error-related Potential Classifier Offers a Comparable Performance to a Personalized Classifier.

Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performance.A major inconvenience of BCIs is that they commonly require a long calibration period, before the user can receive feedback of their own brain signals. Here, we use the data of 15 participants and compare the performance of a personalized ErrP classifier with a generic ErrP classifier. We concluded that there was no significant difference in classification performance between the generic and the personalized classifiers (Wilcoxon signed rank tests, two-sided and one-sided left and right). This results indicate that the use of a generic ErrP classifier is a good strategy to remove the calibration period of a ErrP classifier, allowing participants to receive immediate feedback of the ErrP detections.

Assessing the impact of vibrotactile kinaesthetic feedback on electroencephalographic signals in a center-out task.

OBJECTIVE: An important part of restoring motor control via a brain-computer interface is to close the sensorimotor feedback loop. As part of our investigations into vibrotactile kinaesthetic feedback of arm movements, we studied electroencephalographic signals in the δ, µ and ß bands obtained during a center-out movement task with four conditions: movement with real-time kinaesthetic feedback, movement with static vibrations, movement without vibrotactile input, and no movement with sham feedback. APPROACH: Participants performed center-out movements with their palm on a flat table surface. One of three movement directions was cued visually before the movement. The palm position was tracked in order to provide real-time vibrotactile feedback. All analyses were performed offline. MAIN RESULTS: Movement-related cortical potentials exhibit minor discrepancies between movement conditions as well as between movement directions, in peak amplitude and shape. Classification of each movement condition and each direction against rest yields peak accuracies of 60%-65% using low-frequency amplitude features, and 90% using µ and ß power features. Within-class accuracies of four-way classification between conditions based on low-frequency amplitude features are around chance level for the movement conditions with vibrotactile stimulation, slightly above chance level for the movement condition without stimulation, and considerably higher for the non-movement condition. Four-way classification between conditions based on µ and ß power features yields within-class accuracies slightly above chance level for all movement conditions, and considerably higher for the non-movement condition. Within-class accuracies of three-way classification between directions are slightly above chance level for low-frequency amplitude features, and at chance level for power features. SIGNIFICANCE: We found that the vibrotactile stimulation does not interfere with movement-related features in the δ, µ and ß frequency ranges. Our feedback system may therefore feasibly be deployed in conjunction with a BCI based on movement-related cortical potentials or sensorimotor rhythms, without adversely affecting control.

Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm.

OBJECTIVE: Continuous decoding of voluntary movement is desirable for closed-loop, natural control of neuroprostheses. Recent studies showed the possibility to reconstruct the hand trajectories from low-frequency (LF) electroencephalographic (EEG) signals. So far this has only been performed offline. Here, we attempt for the first time continuous online control of a robotic arm with LF-EEG-based decoded movements. APPROACH: The study involved ten healthy participants, asked to track a moving target by controlling a robotic arm. At the beginning of the experiment, the robot was fully controlled by the participant's hand trajectories. After calibrating the decoding model, that control was gradually replaced by LF-EEG-based decoded trajectories, first with 33%, 66% and finally 100% EEG control. Likewise with other offline studies, we regressed the movement parameters (two-dimensional positions, velocities, and accelerations) from the EEG with partial least squares (PLS) regression. To integrate the information from the different movement parameters, we introduced a combined PLS and Kalman filtering approach (named PLSKF). MAIN RESULTS: We obtained moderate yet overall significant (α = 0.05) online correlations between hand kinematics and PLSKF-decoded trajectories of 0.32 on average. With respect to PLS regression alone, the PLSKF had a stable correlation increase of Δr = 0.049 on average, demonstrating the successful integration of different models. Parieto-occipital activations were highlighted for the velocity and acceleration decoder patterns. The level of robot control was above chance in all conditions. Participants finally reported to feel enough control to be able to improve with training, even in the 100% EEG condition. SIGNIFICANCE: Continuous LF-EEG-based movement decoding for the online control of a robotic arm was achieved for the first time. The potential bottlenecks arising when switching from offline to online decoding, and possible solutions, were described. The effect of the PLSKF and its extensibility to different experimental designs were discussed.

Distinct cortical networks for hand movement initiation and directional processing: An EEG study.

Movement preparation and initiation have been shown to involve large scale brain networks. Recent findings suggest that movement preparation and initiation are represented in functionally distinct cortical networks. In electroencephalographic (EEG) recordings, movement initiation is reflected as a strong negative potential at medial central channels that is phase-locked to the movement onset - the movement-related cortical potential (MRCP). Movement preparation describes the process of transforming high level movement goals to low level commands. An integral part of this transformation process is directional processing (i.e., where to move). The processing of movement direction during visuomotor and oculomotor tasks is associated with medial parieto-occipital cortex (PO) activity, phase-locked to the presentation of potential movement goals. We surmised that the network generating the MRCP (movement initiation) would encode less information about movement direction than the parieto-occipital network processing movement direction. Here, we studied delta band EEG activity during center-out reaching movements (2D; 4 directions) in visuomotor and oculomotor tasks. In 15 healthy participants, we found a consistent representation of movement direction in PO 300-400 â€‹ms after the direction cue irrespective of the task. Despite generating the MRCP, sensorimotor areas (SM) encoded less information about the movement direction than PO. Moreover, the encoded directional information in SM was less consistent across participants and specific to the visuomotor task. In a classification approach, we could infer the four movement directions from the delta band EEG activity with moderate accuracies up to 55.9%. The accuracies for cue-aligned data were significantly higher than for movement onset-aligned data in either task, which also suggests a stronger representation of movement direction during movement preparation. Therefore, we present direct evidence that EEG delta band amplitude modulations carry information about both arm movement initiation and movement direction, and that they are represented in two distinct cortical networks.

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

Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Here, we introduce the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm that can correct these eye artifacts offline and in real time. We provide an open source reference implementation of the algorithm and the paradigm to obtain calibration data. Once the algorithm is fitted to calibration data (approx. 5 â€‹min), the eye artifact correction reduces to a matrix multiplication. We compared SGEYESUB with 4 state-of-the-art algorithms using M/EEG activity of 69 participants. SGEYESUB achieved the best trade-off between correcting the eye artifacts and preserving brain activity. Residual correlations between the corrected M/EEG channels and the eye artifacts were below 0.1. Error-related and movement-related cortical potentials were attenuated by less than 0.5 â€‹µV. Our results furthermore demonstrate that CRD and eyelid-related artifacts can be assumed to be stationary for at least 1-1.5 â€‹h, validating the feasibility of our approach in offline and online eye artifact correction.

Using machine learning to reveal the population vector from EEG signals.

OBJECTIVE: Since the discovery of the population vector that directly relates neural spiking activity with arm movement direction, it has become feasible to control robotic arms and neuroprostheses using invasively recorded brain signals. For non-invasive approaches, a direct relation between human brain signals and arm movement direction is yet to be established. APPROACH: Here, we investigated electroencephalographic (EEG) signals in temporal and spectral domains in a continuous, circular arm movement task. Using machine learning methods that respect the linear mixture of brain activity within EEG signals, we show that directional information is represented in the temporal domain in amplitude modulations of the same frequency as the arm movement, and in the spectral domain in power modulations of the 20-24 Hz frequency band. MAIN RESULTS: In the temporal domain, the directional information was mainly expressed in primary sensorimotor cortex (SM1) and posterior parietal cortex (PPC) contralateral to the moving arm, while in the spectral domain SM1 and PPC of both hemispheres predicted arm movement direction. The different cortical representations suggest distinct neural representations in both domains. SIGNIFICANCE: This direct relation between neural activity and arm movement direction in both domains demonstrates the potential of machine learning to reveal neuroscientific insights about the dynamics of human arm movements.

Online asynchronous decoding of error-related potentials during the continuous control of a robot.

Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.

HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm.

A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact removal (HEAR) algorithm to remove transient electrode pop and drift (PD) artifacts from electroencephalographic (EEG) signals. Transient PD artifacts reflect impedance variations at the electrode scalp interface that are caused by ion concentration changes. HEAR and its online version (oHEAR) are open-source and publicly available. Both outperformed state of the art offline and online transient, high-variance artifact correction algorithms for simulated EEG signals. (o)HEAR attenuated PD artifacts by approx. 25 dB, and at the same time maintained a high SNR during PD artifact-free periods. For real-world EEG data, (o)HEAR reduced the fraction of outlier trials by half and maintained the waveform of a movement related cortical potential during a center-out reaching task. In the case of BCI training, using oHEAR can improve the reliability of the feedback a user receives through reducing a potential negative impact of PD artifacts.

Tuning characteristics of low-frequency EEG to positions and velocities in visuomotor and oculomotor tracking tasks.

Movement decoders exploit the tuning of neural activity to various movement parameters with the ultimate goal of controlling end-effector action. Invasive approaches, typically relying on spiking activity, have demonstrated feasibility. Results of recent functional neuroimaging studies suggest that information about movement parameters is even accessible non-invasively in the form of low-frequency brain signals. However, their spatiotemporal tuning characteristics to single movement parameters are still unclear. Here, we extend the current understanding of low-frequency electroencephalography (EEG) tuning to position and velocity signals. We recorded EEG from 15 healthy participants while they performed visuomotor and oculomotor pursuit tracking tasks. Linear decoders, fitted to EEG signals in the frequency range of the tracking movements, predicted positions and velocities with moderate correlations (0.2-0.4; above chance level) in both tasks. Predictive activity in terms of decoder patterns was significant in superior parietal and parieto-occipital areas in both tasks. By contrasting the two tracking tasks, we found that predictive activity in contralateral primary sensorimotor and premotor areas exhibited significantly larger tuning to end-effector velocity when the visuomotor tracking task was performed.

Exploring representations of human grasping in neural, muscle and kinematic signals.

Movement covariates, such as electromyographic or kinematic activity, have been proposed as candidates for the neural representation of hand control. However, it remains unclear how these movement covariates are reflected in electroencephalographic (EEG) activity during different stages of grasping movements. In this exploratory study, we simultaneously acquired EEG, kinematic and electromyographic recordings of human subjects performing 33 types of grasps, yielding the largest such dataset to date. We observed that EEG activity reflected different movement covariates in different stages of grasping. During the pre-shaping stage, centro-parietal EEG in the lower beta frequency band reflected the object's shape and size, whereas during the finalization and holding stages, contralateral parietal EEG in the mu frequency band reflected muscle activity. These findings contribute to the understanding of the temporal organization of neural grasping patterns, and could inform the design of noninvasive neuroprosthetics and brain-computer interfaces with more natural control.

Masked and unmasked error-related potentials during continuous control and feedback.

The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain-computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages. OBJECTIVE: We developed a task in which subjects have continuous control of a cursor's position by means of a joystick. The cursor's position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals. APPROACH: This paper studies the electroencephalographic (EEG)-measurable signatures caused by a loss of control over the cursor's trajectory, causing a target miss. MAIN RESULTS: In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-[Formula: see text], average TPR = 81.8% and average TNR = 96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-[Formula: see text], average TPR = 60.9% and average TNR = 58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%. SIGNIFICANCE: The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.