Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.

The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit.

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.

Introduction: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.

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

. 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.

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.

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.

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

Unlabelled: 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.

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.

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.

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 β 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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets.

In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted.

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

Unlabelled: 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.

Decoding natural reach-and-grasp actions from human EEG.

Objective: Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates.

Approach: In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects.

Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects.

Objective: One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention.

Approach: We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions.

EEG neural correlates of goal-directed movement intention.

Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement.

Intersession adaptation of the EEG-based detector of self-paced walking intention in stroke patients.

Brain-computer interfaces (BCIs) have been used in patients with motor impairments as a rehabilitation tool, allowing the control of prosthetic devices with their brain signals. Typically, before each rehabilitation session a calibration phase is recorded to account for session-specific signal changes. Calibration is often an inconvenient process due to its length and patients' fatigue-proneness.

Detecting intention to walk in stroke patients from pre-movement EEG correlates.

Background: Most studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI.

Methods: We investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy.