Movement-related Cortical Potentials Research Articles

Preparatory movement state enhances premovement EEG representations for brain-computer interfaces.

&#xD;Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions. &#xD;Methods. &#xD;Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task‑related common spatial patterns (TR-CSP) and common spatial patterns (CSP) algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy. &#xD;In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p<0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95. &#xD;Significance. &#xD;The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.

Movement-related cortical potential and speech-induced suppression during speech production in younger and older adults

With age, the speech system undergoes important changes that render speech production more laborious, slower and often less intelligible. And yet, the neural mechanisms that underlie these age-related changes remain unclear. In this EEG study, we examined two important mechanisms in speech motor control: pre-speech movement-related cortical potential (MRCP), which reflects speech motor planning, and speaking-induced suppression (SIS), which indexes auditory predictions of speech motor commands, in 20 healthy young and 20 healthy older adults. Participants undertook a vowel production task which was followed by passive listening of their own recorded vowels. Our results revealed extensive differences in MRCP in older compared to younger adults. Further, while longer latencies were observed in older adults on N1 and P2, in contrast, the SIS was preserved. The observed reduced MRCP appears as a potential explanatory mechanism for the known age-related slowing of speech production, while preserved SIS suggests intact motor-to-auditory integration.

Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy.

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.

Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing

Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.

Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG. Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia. Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention. The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

Comparing movement-related cortical potential between real and simulated movement tasks from an ecological validity perspective.

Concerns regarding the ecological validity of movement-related cortical potential (MRCP) experimental tasks that are related to motor learning have recently been growing. Therefore, we compared MRCP during real movement task (RMT) and simulated movement task (SMT) from an ecological validity perspective. The participants performed both RMT and SMT, and MRCP were measured using electroencephalogram (EEG). EEG was based on the 10-20 method, with electrodes placed in the motor cortex (C3 and C4) and supplementary motor cortex (FCz [between Fz and Cz] and Cz) areas. This experiment examined the MRCP using Bereitschaftspotential (BP) and negative slope (NS') onset times, and BP, NS', and motor potential (MP) amplitudes during the task. The results revealed that the SMT exhibited later BP and NS' onset times and smaller BP, NS', and MP amplitudes than the RMT. Furthermore, in RMT, the onset time of MRCP was delayed, and the amplitude of MRCP was smaller in the second half of the 200 times task than in the first half, whereas in SMT, there was no change in onset time and amplitude. The SMT showed a different MRCP than the RMT, suggesting that the ecological validity of the task should be fully considered when investigating the cortical activity associated with motor skill learning using MRCP. Ecological validity of the study should be fully considered when investigating the cortical activity associated with motor skill learning using MRCP. Moreover, it is important to understand the differences between the two methods when applied clinically.

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals.

Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

Event-Related Brain Potentials N140 and P300 during Somatosensory Go/NoGo Tasks Are Modulated by Movement Preparation.

The Go/NoGo task requires attention and sensory processing to distinguish a motor action cue or 'Go stimulus' from a 'NoGo stimulus' requiring no action, as well as motor preparation for a rapid Go stimulus response. The neural activity mediating these response phases can be examined non-invasively by measuring specific event-related brain potentials (ERPs) using electroencephalography. However, it is critical to determine how different task conditions, such as the relationship between attention site and movement site, influence ERPs and task performance. In this study, we compared attention-associated ERP components N140 and P300, the performance metrics reaction time (RT) and accuracy (%Error) and movement-related cortical potentials (MRCPs) between Go/NoGo task trials in which attention target and movement site were the same (right index finger movement in response to right index finger stimulation) or different (right index finger movement in response to fifth finger stimulation). In other Count trials, participants kept a running count of target stimuli presented but did not initiate a motor response. The N140 amplitudes at electrode site Cz were significantly larger in Movement trials than in Count trials regardless of the stimulation site-movement site condition. In contrast, the P300 amplitude at Cz was significantly smaller in Movement trials than in Count trials. The temporal windows of N140 and P300 overlapped with the MRCP. This superposition may influence N140 and P300 through summation, possibly independent of changes in attentional allocation.

Modulatory effects of transcutaneous auricular vagus nerve stimulation (taVNS) on attentional processes

BackgroundThe modulatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) on attention has varied in previous studies. This inconsistency might be attributed to the combined influence of the modulation effect...

Decoding Electroencephalography Underlying Natural Grasp Tasks across Multiple Dimensions

Individuals suffering from motor dysfunction due to various diseases often face challenges in performing essential activities such as grasping objects using their upper limbs, eating, writing, and more. This limitation significantly impacts their ability to live independently. Brain–computer interfaces offer a promising solution, enabling them to interact with the external environment in a meaningful way. This exploration focused on decoding the electroencephalography of natural grasp tasks across three dimensions: movement-related cortical potentials, event-related desynchronization/synchronization, and brain functional connectivity, aiming to provide assistance for the development of intelligent assistive devices controlled by electroencephalography signals generated during natural movements. Furthermore, electrode selection was conducted using global coupling strength, and a random forest classification model was employed to decode three types of natural grasp tasks (palmar grasp, lateral grasp, and rest state). The results indicated that a noteworthy lateralization phenomenon in brain activity emerged, which is closely associated with the right or left of the executive hand. The reorganization of the frontal region is closely associated with external visual stimuli and the central and parietal regions play a crucial role in the process of motor execution. An overall average classification accuracy of 80.3% was achieved in a natural grasp task involving eight subjects.

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.

In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

Can water-based EEG caps record robust movement-related cortical potentials (MRCPs) for single and multiple joint movements?

Movement-related cortical potentials (MRCPs) have been used extensively in the literature to develop rehabilitation interventions for people with neurological conditions. In this pilot study, we recorded and extracted MRCPs using a water-based cap to determine whether water-based caps are effective. Five participants took part in the study, where their EEG was recorded during single-joint (dorsiflexion) and multiple-joint (sit-to-stand) lower limb movements. We were able to see clear MRCPs for both movement types with an average peak negativity (PN) latency of +22ms for dorsiflexion and +218ms for sit-to-stand. Similarly, the PN amplitude of -14.89μV was recorded for dorsiflexion and -43.54μV for sit-to-stand. These values were comparable to the values reported in studies using gel-based caps. Based on these results, water-based caps can be an effective way to produce robust MRCPs, which can have many advantages over gel-based caps.Clinical Relevance- The study provides clinicians with a more viable method of collecting EEGs and extracting MRCPs, thus allowing them to design more robust interventions for people with neurological disorders.

Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution.

Spinal Cord Injury (SCI) is a common disease that usually limits the patient's independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs' amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients' mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature's amplitudes in the latter group.Clinical Relevance- This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.

Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Movement-Related Cortical Potentials (MRCP) are signals that begin to appear approximately two seconds before the onset of voluntary movements and can be recorded with EEG. MRCP is an important sign that the movement will begin. Determining the movement intention before the action is extremely important information especially for real-time BCI systems. By using MRCP, Brain-Computer Interface (BCI) users' movement intention can be determined prior to the move and this sign can be used as a control signal. In this study, it was aimed to determine the movement and resting states with high accuracy with MRCP signals. Furthermore, the effects of filter cutoff frequencies, number of electrodes, and MRCP time interval window on the success of distinguishing movement/resting states in the preprocessing stage were investigated. For this purpose, Katz fractal dimension and nonlinear support vector machine methods were used in the feature extraction and classification stages, respectively. The proposed method was tested on the attempted hand and arm movements dataset containing EEG signals of 10 participants with spinal cord injury. Katz fractal dimension and support vector machines methods can determine movement and resting states with an average of 96.47% accuracy using MRCP signals. If the number of electrodes to be used in signal analysis was 3, 9 and 61, the obtained accuracy rates were determined as 83.71%, 90.67%, and 96.47%, respectively. The experimental results also showed that the filter cutoff frequencies used in the preprocessing had a significant effect on the accuracy.

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications.

An active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface

Movement function rehabilitation of patients with craniocerebral injuries is an important issue facing neurorehabilitation science. The use of brain–computer interface technology in rehabilitation training systems can allow patients to actively participate in the rehabilitation training process and use the brain’s neuroplasticity to enhance the effects from rehabilitation training. At present, the brain–computer interface-based rehabilitation training system still has problems such as insufficient active participation of patients, resulting in slowed motor neural circuit repair or low action execution accuracy. In response to the above problems, this paper designed an active and passive upper limb rehabilitation training system based on a hybrid brain–computer interface of steady-state visual evoked potentials (SSVEP) and movement-related cortical potentials (MRCPs). The system includes six parts: task setting and training guidance module, EEG signal acquisition module, EEG signal preprocessing and recognition module, rehabilitation training module, training completion evaluation module, and communication module. The system drives the rehabilitation robot to complete the training actions by identifying the participant’s SSVEP and evaluates the completion of the rehabilitation training based on the patient’s movement intention recognition results. In this study, 12 participants were recruited. In the online test, the system achieved an average action execution accuracy of 99.3%. The movement intention detection based on MRCPs reached an average accuracy of 82.7%. The participants’ average completion rate was 0.91. The experimental results show that the system can achieve a high rate of execution accuracy. In addition, it can evaluate the active participation level of patients in rehabilitation training based on the movement intention detection results, accelerate the reconstruction of motor neural circuits, improve the effects of training, and provide more effective ways of thinking for the study of upper limb rehabilitation training systems for patients with craniocerebral injuries.

The effect of galvanic vestibular stimulation on the movement-related cortical potential in Parkinson’s disease

The effect of galvanic vestibular stimulation on the movement-related cortical potential in Parkinson’s disease

Decoding Coordinated Directions of Bimanual Movements From EEG Signals.

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35 %. The binary classification accuracies achieved 80.24 ± 6.25 , 82.62 ± 7.82 , and 86.28 ± 5.50 % for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50 %, 75.67 ± 7.18 %, and 77.79 ± 5.65 %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

Decoding Natural Grasping Behaviors: Insights Into MRCP Source Features and Coupling Dynamics.

The effective decoding of natural grasping behaviors is crucial for the natural control of neural prosthetics. This study aims to investigate the decoding performance of movement-related cortical potential (MRCP) source features between complex grasping actions and explore the temporal and frequency differences in inter-muscular and cortical-muscular coupling strength during movement. Based on the human grasping taxonomy and their frequency, five natural grasping motions-medium wrap, adducted thumb, adduction grip, tip pinch, and writing tripod-were chosen. We collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants, and projected six EEG frequency bands into source space for further analysis. Results from multi-classification and binary classification demonstrated that MRCP source features could not only distinguish between power grasp and precision grasp, but also detect subtle action differences such as thumb adduction and abduction during the execution phase. Besides, we found that during natural reach-and-grasp movement, the coupling strength from cortical to muscle is lower than that from muscle to cortical, except in the hold phase of γ frequency band. Furthermore, a 12-Hz peak of inter-muscular coupling strength was found in movement execution, which might be related to movement planning and execution. We believe that this research will enhance our comprehension of the control and feedback mechanisms of human hand grasping and contributes to a natural and intuitive control for brain-computer interface.

MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements.

Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.