Movement-related Cortical Potentials Research Articles

Evaluation of Multi-layer Perceptron Neural Networks in Predicting Ankle Dorsiflexion in Healthy Adults using Movement-related Cortical Potentials for BCI-Neurofeedback Applications.

Brain computer interface (BCI) systems were initially developed to replace lost function; however, they are being increasingly utilized in rehabilitation to restore motor functioning after brain injury. In such BCI-mediated neurofeedback training (BCI-NFT), the brain-state associated with movement attempt or intention is used to activate an external device which assists the movement while providing sensory feedback to enhance neuroplasticity. A critical element in the success of BCI-NFT is accurate timing of the feedback within the active period of the brain state. The overarching goal of this work was to develop a reliable deep learning model that can predict motion before its onset, and thereby deliver the sensory stimuli in a timely manner for BCI-NFT applications. To this end, the main objective of the current study was to design and evaluate a Multi-layer Perceptron Neural Network (MLP-NN). Movement-related cortical potentials (MRCP) during planning and execution of ankle dorsiflexion was used to train the model to classify dorsiflexion planning vs. rest. The accuracy and reliability of the model was evaluated offline using data from eight healthy individuals (age: 26.3 ± 7.6 years). First, we evaluated three different epoching strategies for defining our 2 classes, to identify the one which best discriminated rest from dorsiflexion. The best model accuracy for predicting ankle dorsiflexion from EEG before movement execution was 84.7%. Second, the effect of various spatial filters on the model accuracy was evaluated, demonstrating that the spatial filtering had minimal effect on model accuracy and reliability.

Lateralized readiness potentials can identify hemisphere of recovery in stroke patients.

Event related cortical potentials related to motor action are referred to as movement related cortical potentials. The late component of which is the readiness potential (RP) and its polarity is more negative in the hemisphere responsible for planning of motor action. This lateralized nature of RP during unilateral hand movement is studied as lateralized readiness potential (LRP) by calculating the contralateral-minus-ipsilateral difference wave for each hand. The aim was to identify the hemisphere contributing to motor recovery in acute and chronic stroke patients through recording LRPs. Twenty-nine cases with cerebrovascular stroke (15 acute and 14 chronic) were included in the study. EEG was recorded in response to self-cued button presses by the paretic side to obtain the averaged LRP amplitude. The hemisphere with greater negativity was considered the side of recovery. Functional recovery was assessed by Fugl Meyer test. In acute cases, recovery was more related to LRP activity in the contralesional hemisphere (73%), whereas lateralization was equal in chronic cases; 50% in either group. LRP amplitude was higher in the contralesional hemisphere (p = 0.02). Functional recovery assessed by the Fugl Meyer test (FM) was similar whether recovery was ipsi- or contralesional. Early after stroke, motor recovery is more likely to involve compensatory activity in the contralesional hemisphere, while in the chronic phase, the ipsilesional hemisphere may recover its function and become more active. Further research is needed to verify if the technique mentioned in our study could be used to guide customized NIBS protocols tailoring the optimal site and parameters for each patient.

IENet: a robust convolutional neural network for EEG based brain-computer interfaces

Objective. Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop into novel application areas with more complex scenarios, which put forward higher requirements for the robustness of EEG signal processing algorithms. Deep learning can automatically extract discriminative features and potential dependencies via deep structures, demonstrating strong analytical capabilities in numerous domains such as computer vision and natural language processing. Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper. Approach. Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field (RF) gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost. We compare with the current state-of-the-art methods across five EEG-BCI paradigms: steady-state visual evoked potentials (VEPs), epilepsy EEG, overt attention P300 VEPs, covert attention P300 visual-EPs and movement-related cortical potentials. Main results. The classification results show that the generalizability of IENet is on par with the state-of-the-art paradigm-agnostic models on test datasets. Furthermore, the feature explainability analysis of IENet illustrates its capability to extract neurophysiologically interpretable features for different BCI paradigms, ensuring the reliability of algorithm. Significance. It can be seen from our results that IENet can generalize to different BCI paradigms. And it is essential for deep CNNs to increase the RF size using average RF gain.

Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement.

Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

Non-linear optimized spatial filter for single-trial identification of movement related cortical potential

To investigate the optimal filter settings for pre-processing of Movement Related Cortical Potentials (MRCP) for the detection through EEG in single trial, we have proposed a novel Non-Linear Optimized Spatial Filter (NL-SF) and compared it to the Optimized Spatial Filtering (OSF) used in literature. MRCPs from EEG recordings are emphasized, calculating the optimal non-linear combination of channels which isolates the signal of interest. The method is applied to EEG data recorded from 16 healthy patients either executing or imagining 50 self-paced upper limb movements (palmar grasp). MRCPs have been identified from the outputs of the two filters by matching with a template built by averaging responses to movement intentions in the training set. NL-SF had a median accuracy on the overall dataset of 84.6%, which is significantly better than that of OSF (i.e., 76.9%). Being a filter and feasible for self-paced applications, it could be of interest in online BCI system design.

Effects of Transcutaneous Vagus Nerve Stimulation (tVNS) on Action Planning: A Behavioural and EEG Study.

Action planning is an important decision-making process, which can be specially affected by environment. Response selection during action planning has been demonstrated to be modulated by tVNS. Therefore, tVNS shows a great potential for modulating the action planning process. We aimed to explore the tVNS-induced effect on action planning in behavioural and electrophysiology. Twenty-eight participants were randomly divided into two groups (active group and sham group). A single-blind, sham-controlled between-subject design was applied to explore the effect of online-tVNS (i.e., tVNS overlapping with the task) on action planning paradigm. We measured and compared reaction time (RT) and movement-related cortical potentials (MRCPs) before and after tVNS between active and sham groups. As compared to sham group, for the ipsilateral hand/contralateral hemisphere relative to the stimulated side, active tVNS significantly reduced the reaction time and decreased the MRCP amplitude mainly in the challenging tasks. Our results indicate that tVNS can produce a lateralization effect on action planning, especially plays an important role in the more challenging tasks as reflected both in the behavioural and electrophysiological results.

Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG.

Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten individuals without a disability) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.

Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding.

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.

Obstacle avoidance movement-related motor cortical activity with cognitive task.

Lack of attention to obstacles on the floor or walking path may cause trip and fall accidents. The preparatory activity in the motor cortex to the perturbation associated with obstacle avoidance movements with cognitive task is still unclear. The purpose of this study was to investigate the motor cortical activity involved in the preparation and execution of concurrent obstacle avoidance movement and cognitive task. Twenty young adults were required to step over obstacles that were projected on the floor while performing a cognitive task. The electroencephalogram was recorded, and the movement-related cortical potentials (MRCP) aligned by foot dorsiflexion were evaluated. There was no significant difference in the number of contacts between the toe and the obstacle between the obstacle avoidance task and obstacle avoidance with cognitive task; however, the distance between the toe and the obstacle just before obstacle avoidance movement was significantly extended in the latter task. The amplitude and the onset of MRCP during the dual task were decreased and delayed, respectively, compared with the simple obstacle avoidance movement task. These results suggest that the young participants changed their clearance strategy to stepping over the obstacle during the concurrent motor and cognitive dual task to reduce motor cortical activity.

Focal stimulation of movement related cortical potentials in mice using a novel TMS circuit design

Focal stimulation of movement related cortical potentials in mice using a novel TMS circuit design

Movement related cortical potentials and meaningful drawing

Movement related cortical potentials are usually studied employing single and well defined movements, thus lacking adherence to reality of complex gestures. Our research was aimed at investigating the possibility of triggering EEG recordings during natural freehand drawing and assessing if differences could be established between meaningful and meaningless movements.

Movement-related EEG signatures associated with freezing of gait in Parkinson's disease: an integrative analysis.

Freezing of gait is the most severe gait deficit associated with Parkinson’s disease and significantly affects patients’ independence and consequently their quality of life. The lack of a clear understanding of its underlying neurophysiological mechanism has resulted in limited effectiveness of the current treatment options. In this study, we investigated EEG features over (pre-)supplementary motor area and primary motor cortex during a simple cue-based ankle dorsiflexion movement. These features include movement-related cortical potentials (0.05–5 Hz) and brain oscillations (1–50 Hz). Electromyogram signal from the tibialis anterior muscle of the dominant foot was used to determine the movement onset. The EEG features before, during and following the onset of the movement were compared among three groups of participants: patients with freezing (N = 14, 11 males), patients without freezing (N = 14, 13 males) and healthy age-matched controls (N = 13, 10 males) with 15 recorded trials for each individual. Additionally, Parkinson’s disease patients with freezing of gait were separated into mild (N = 7) and severe cases (N = 5), so that EEG features associated with freezing severity could be investigated. The results indicated significant differences between patients with severe freezing of gait compared to healthy controls and patients without freezing of gait. In addition, patients with mild and severe freezing represented cortical activity differences. For patients with freezing, the initial component of movement-related cortical potential is significantly lower than that of the healthy controls (P = 0.002) and is affected by the severity of freezing. Furthermore, a striking absence of beta frequency band (12–35 Hz) desynchronization was observed in patients with freezing, especially low-beta frequency band over Cz, before the movement, which was also associated with the severity of the freezing of gait. Low-beta (13–20 Hz) and high-beta (21–35 Hz) frequency band activities represented unique features for each group. Beta event-related desynchronization over Cz present in healthy controls prior to movement onset, was partially replaced by the theta band (4–8 Hz) synchrony in patients with freezing. Patients with severe freezing also represented some level of theta band synchronization over contralateral supplementary motor area. This suggests the involvement of cognitive processing over the motor cortex in controlling cue-based voluntary movement as a compensatory mechanism associated with freezing of gait. The EEG features identified in this study are indicative of important freezing of gait clinical characteristics such as severity and contribute to a better understanding of the underlying neurophysiology of the mysterious phenomenon of freezing of gait.

Electroencephalographic Recording of the Movement-Related Cortical Potential in Ecologically Valid Movements: A Scoping Review.

The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson’s Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting.

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram.

Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3–3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human–machine interaction system, such as rehabilitation robot.

Neurophysiology of Grasping Habits: Evidence From Movement-Related Cortical Potentials

Neurophysiology of Grasping Habits: Evidence From Movement-Related Cortical Potentials

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Objective. Brain–computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis. Approach. Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis. Main results. The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%). Significance. The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

The bilateral limb deficit (BLD) phenomenon during leg press: a preliminary investigation into central and peripheral factors

BackgroundThe bilateral limb deficit (BLD) phenomenon suggests that lower forces are produced with bilateral limb contractions compared to the summed force produced when the same muscles are contracted unilaterally. While interhemispheric inhibition has been suggested as a cause of BLD, the origin of the deficit is yet to be determined. The aim of this study was to investigate central and peripheral factors responsible for the BLD during leg press using surface electromyography (EMG) and electroencephalography (EEG).MethodsFourteen adults (age = 23.7 ± 4.7 years old) completed bilateral (BL), unilateral left (UL) and unilateral right (UR) isometric leg press exercises. Bilateral limb ratio (BLR) was calculated similar to previous studies and surface EMG from three muscles of the quadriceps femoris (vastus lateralis, vastus medialis and rectus femoris) was used to measure the level of muscle activation. Movement related cortical potentials (MRCPs) over the left and right motor cortex areas (C3 and C4, respectively) were used to assess brain activity asymmetries reflecting central factors.ResultsNo significant difference was noted in the mean BLR (BLR = 94.8%), but a subset of ten participants did demonstrate a BLD (BLR = 81.4%, p < 0.01). Mean differences in relative activation were found among the three quadricep muscles (p < 0.001) with the right VM having significantly higher amplitude for the unilateral right (0.347 ± 0.318 mV) and bilateral right (0.436 ± 0.470 mV) conditions, respectively) than either the VL or RF (p < 0.05). The VL had significantly lower amplitudes in all conditions (0.127 ± 0.138 mV; 0.111 ± 0.104 mV; 0.120 ± 0.105 mV; 0.162 ± 0.147 mV for unilateral left, bilateral left, unilateral right, and bilateral right, respectively). However no overall significant differences were noted between bilateral and unilateral conditions. No significant differences in MRCPs were observed between brain activity of the C3 and C4 electrodes in any of the conditions.ConclusionWhile the sample size was low, this exploratory study noted the presence of BLD however the results did not provide evidence of significant limitations in either the EMG or EEG data.

Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals

Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain–computer interfaces (BCIs) for people with motor disabilities. Objective. The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues. Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously. Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas. Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants’ behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.

Movement-Related Cortical Potentials in Embodied Virtual Mirror Visual Feedback

Background: Mirror therapy is thought to drive interhemispheric communication, resulting in a balanced activation. We hypothesized that embodied virtual mirror visual feedback (VR-MVF) presented on a computer screen may produce a similar activation. In this proof-of-concept study, we investigated differences in movement-related cortical potentials (MRCPs) in the electroencephalogram (EEG) from different visual feedback of user movements in 1 stroke patient and 13 age-matched adults.Methods: A 60-year-old right-handed (Edinburgh score >95) male ischemic stroke [left paramedian pontine, National Institutes of Health Stroke Scale (NIHSS) = 6] patient and 13 age-matched right-handed (Edinburgh score >80) healthy adults (58 ± 9 years; six female) participated in the study. We recorded 16-electrode electroencephalogram (EEG), while participants performed planar center-out movements in two embodied visual feedback conditions: (i) direct (movements translated to the avatar's ipsilateral side) and (ii) mirror (movements translated to the avatar's contralateral side) with left (direct left/mirror left) or right (direct right/mirror right) arms.Results: As hypothesized, we observed more balanced MRCP hemispheric negativity in the mirror right compared to the direct right condition [statistically significant at the FC4 electrode; 99.9% CI, (0.81, 13)]. MRCPs in the stroke participant showed reduced lateralized negativity in the direct left (non-paretic) situation compared to healthy participants. Interestingly, the potentials were stronger in the mirror left (non-paretic) compared to direct left case, with significantly more bilateral negativity at FC3 [95% CI (0.758 13.2)] and C2 [95% CI (0.04 9.52)].Conclusions: Embodied mirror visual feedback is likely to influence bilateral sensorimotor cortical subthreshold activity during movement preparation and execution observed in MRCPs in both healthy participants and a stroke patient.

The effect of acute aerobic exercise on the consolidation of motor memories.

Acute aerobic exercise performed prior to training may assist with motor skill acquisition through enhancement of motor cortical plasticity. In addition, high-intensity exercise performed after training improves retention, although the mechanisms of this are unclear. We hypothesized that acute continuous moderate-intensity exercise performed post-motor training would also assist with motor skill retention and that this behavioral change would be positively correlated with neural markers of training-related cortical adaptation. Participants [n = 33; assigned to an exercise (EXE) or control (CON) group] completed a single visuomotor training session using bilateral wrist movements while movement-related cortical potentials (MRCPs) were collected. After motor training, the EXE group exercised for 20min [70% of heart rate reserve (HRR)] and the CON group read for the same amount of time. Both groups completed two post-training tests after exercise/rest: 10min and ~ 30min once heart rate returned to resting level in EXE. Retention and transfer tests were both completed 1 and 7days later. MRCPs measured training-related neural adaptations during the first visit and motor performance was assessed as time and trajectory to the target. The EXE group had better performance than CON at retention (significant 7days post-training). MRCP amplitudes increased from early to late motor training and this amplitude change was correlated with motor performance at retention. Results suggest that moderate-intensity exercise post-motor training helps motor skill retention and that there may be a relationship with motor training-related cortical adaptations that is enhanced with post-motor training exercise.