Quantification of Movement-Related EEG Correlates Associated with Motor Training: A Study on Movement-Related Cortical Potentials and Sensorimotor Rhythms

The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.

A multimodal approach integrating cognitive and motor demands into physical activity for optimal mental health: Methodological issues and future directions

Physical activity is known for its positive effects on cognition and affect, with existing literature suggesting that these mental health benefits may be optimally experienced by incorporating cognitive and motor demands during physical activity (PA). However, the existing body of literature lacks a comprehensive guideline for designing the qualitative characteristics of a PA program. Accordingly, this narrative review aimed to (1) provide a working two-dimension model that operationally defines the cognitive and motor demands involved in PA and the rationale for systematically studying these qualitative aspects of PA, (2) identify methods to assess the cognitive and motor demands of PA and address associated methodological issues, and (3) offer potential future directions for research on the cognitive and motor aspects of PA in support of the development of PA programs designed to maximize PA-induced cognitive and affective benefits. We anticipate this article to inform the need for future research and development on this topic, aiming to create clear, evidence-based guidelines for designing innovative and effective PA interventions.

Brain-computer interface in critical care and rehabilitation

This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

Age-related Deterioration of Alpha Power in Cortical Areas Slowing Motor Command Formation in Healthy Elderly Subjects

BACKGROUND/AIM

The same neural processes may govern older people's motor and cognitive abilities since an inability to switch between actions develops with aging. In this study, a dexterity test was used to measure motor and cognitive perseverance, which required participants to move their fingers fast and correctly on hole boards.

MATERIALS AND METHODS

An electroencephalography (EEG) recording was used to evaluate how healthy young and older adults process brain signals when performing the test.

RESULTS

A significant difference was found between the young and older groups in the average time taken to complete the test, with the older group taking 87.4 s and the young group taking 55.21 s. During motor movement, young participants showed alpha desynchronization over the cortex (Fz, Cz, Oz, Pz, T5, T6, P3, P4) in comparison to the resting state. However, compared to the younger group, no alpha desynchronization was found in the aging group during motor performance. It was noteworthy that alpha power (Pz, P3, and P4) in the parietal cortex was significantly lower in older compared to young adults.

CONCLUSION

Age-related slowdown in motor performance may be caused by deteriorating alpha activity in the parietal cortex, which functions as a sensorimotor interface. This study provides new insights into how perception and action are distributed between brain regions.

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials

To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

The effect of exergaming on cognition and brain activity in older adults: A motor- related cortical potential study

Exergames have positive effects on various cognitive domains. However, to the best of our knowledge, not only have few studies investigated the exergame-induced brain changes, but also in most of them, preparatory brain activity has not been considered. Preparatory brain activity is a particularly relevant aspect for investigating the interaction between cognitive and sensorimotor functions in the brain. Accordingly, the aim of this study was to investigate the effects of an exergame protocol versus traditional motor-cognitive dual-task training on the cognition and proactive components of movement-related cortical potential. A total of 52 older adults were randomly assigned to the intervention (exergame training) and the control group (motor-cognitive dual-task training). The outcome measurements were neurophysiological data (i.e., the amplitude of the late contingent negative variation [CNV], and alpha/beta event-related desynchronization [ERD]), and neuropsychological data (rate-correct score [RCS] in go/no go task and trail-making test [TMT]). The results revealed that both groups had a decreased late CNV, and alpha/ beta ERD in post-training compared to pre-training in Cz and C3 channels. Moreover, both groups had an increased RCS and a decreased TMT-A in post-training compared to pre-training. However, for TMT-B, the results indicated a significant interaction in favor of the exergame group. These findings indicate that in older adults, both interventions may result in increasing inhibitory control, information processing speed, and preparatory brain activity. However, for cognitive flexibility, exergame has an additional effect relative to the control group.

Time-estimation process could cause the disappearence of readiness potential

Generally, the readiness potential (RP) is considered to be the scalp electroencephalography (EEG) activity preceding movement. In our previous study, we found early RP was absent among approximately half of the subjects during instructed action, but we did not identify the mechanism causing the disappearance of the RP. In this study, we investigated whether the time-estimation process could cause the disappearance of the RP. First, we designed experiments consisting of motor execution (ME), motor execution after time estimation (MEATE), and time estimation (TE) tasks, and we collected and preprocessed the EEG data of 16 subjects. Second, we compared the event related potential (ERP) waveform and scalp topography between ME and MEATE tasks. Then, to explore the influence of time-estimation, we analyzed the difference in ERP between MEATE and TE tasks. Finally, we used source imaging to probe the activation of brain regions during the three tasks, and we calculated the average activation amplitude of eight motor related brain regions. We found that the RP occurred in the ME task but not in the MEATE task. We also found that the waveform of the difference in ERP between the MEATE and TE tasks was similar to that of the ME task. The results of source imaging indicated that, compared to the ME task, the activation amplitude of the supplementary motor area (SMA) decreased significantly for the MEATE task. Our results suggested that the time estimation process could cause the disappearance of the RP. This phenomenon might be caused by the counteraction of neural electrical activity related to time estimation and motor preparation in the SMA.

Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: A proof-of-concept EEG study

OBJECTIVE

Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation (ESS) for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity.

APPROACH

Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. Independent components of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks.

RESULTS

Independent Component analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions.

SIGNIFICANCE

The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.

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 Hand Movement Types and Kinematic Information From Electroencephalogram

Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.

Low-frequency neural activity at rest is correlated with the movement-related cortical potentials elicited during both real and imagined movements

Ongoing low-frequency activity in the brain has been shown to indicate an inhibitory neural state; however, the effects of this low-frequency activity on event-related neural processes associated with movement preparation, including movement-related cortical potentials (MRCPs) or more specifically, the motor potential (MP), and event-related desynchronization (ERD) have not been assessed. Using data from 48 participants, the current study examined how ongoing mu and beta frequency activity at rest relates to the MP and mu and beta ERD during real or imagined movement of the fingers. Resting state EEG activity was collected for 1 min, prior to the real and imagined finger movement trials. 20 real and 20 imagined movement trials were collected for each hand. Resting beta activity correlated with MP amplitude during movement trials for both the right (r(47) = -0.304, p = 0.035) and left (r(47) = -0.468, p < 0.001) hands, whereas resting mu correlated with MP amplitude during motor imagery trials of both the right (r(47) = -0.289, p = 0.046) and left (r(47) = -0.330, p = 0.020) hands. Ongoing mu and beta activity was not significantly correlated with mu or beta ERD for both the movement and imagery trials. A connection between low-frequency activity and MP could inform biofeedback procedures that promote a reduction of this activity, ultimately allowing for easier identification of the intent to move.

Haptic Nudges Increase Affected Upper Limb Movement During Inpatient Stroke Rehabilitation: Multiple-Period Randomized Crossover Study

BACKGROUND

As many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between disability, lost productivity, and ongoing health care costs mean reducing disability after stroke is critical at both individual and society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity monitoring and promotion using wearable devices offer a potential technology-based solution to address this gap. Commonly, wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little is known about the effectiveness of haptic nudging in promoting behaviors in patient populations.

OBJECTIVE

This study aimed to estimate the effect of haptic nudging delivered via a wrist-worn wearable device on UL movement in people with UL disability following stroke undertaking inpatient rehabilitation.

METHODS

A multiple-period randomized crossover design was used to measure the association of UL movement with the occurrence of haptic nudge reminders to move the affected UL in 20 people with stroke undertaking inpatient rehabilitation. UL movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 7:00 AM to 7:00 PM on a single weekday. On 36 occasions, a haptic nudge to move the affected UL was provided just before the observation period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation period following a "Planned Nudge" to the odds of affected limb movement during the observation period following "No Nudge."

RESULTS

The primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge was 1.44 (95% CI 1.28-1.63, P<.001). The secondary analysis revealed an increased odds of affected UL movement following a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI 1.65-2.51, P<.001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI 0.99-1.29, P=.07).

CONCLUSIONS

Haptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions given.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry 12616000654459; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370687&isReview=true.

Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering

An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement ( ) compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.

Electroencephalography correlates of transcranial direct-current stimulation enhanced surgical skill learning: A replication and extension study

Transcranial direct-current stimulation (tDCS), an increasingly applied form of non-invasive brain stimulation, can augment the acquisition of motor skills. Motor learning investigations of tDCS are limited to simple skills, where mechanisms are increasingly understood. Investigations of meaningful, complex motor skills possessed by humans, such as surgical skills, are limited. This replication and extension of our previous findings used electroencephalography (EEG) to determine how tDCS and complex surgical training alters electrical activity in the sensorimotor network to enhance complex surgical skill acquisition. In twenty-two participants, EEG was recorded during baseline performance of simulation-based laparoscopic surgical skills. Participants were randomized to receive 20 min of primary motor cortex targeting anodal tDCS or sham concurrent to 1 h of surgical skill training. EEG was reassessed following training, during a post-training repetition of the surgical tasks. Our results replicated our previous study suggesting that compared to sham, anodal tDCS enhanced the acquisition of unimanual surgical skill. Surgical training modulated delta frequency band activity in sensorimotor regions. Next, the performance of unimanual and bimanual skills evoked unique EEG profiles, primarily within the beta frequency-band in parietal regions. Finally, tDCS-paired surgical training independently modulated delta and alpha frequency-bands in sensorimotor regions. Application of tDCS during surgical skill training is feasible, safe and tolerable. In conclusion, we are the first to explore electrical brain activity during performance of surgical skills, how electrical activity may change during surgical training and how tDCS alters the brain to enhance skill acquisition. The results provide preliminary evidence of neural markers that can be targeted by neuromodulation to optimize complex surgical training.

Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression

The movement-related cortical potential (MRCP) is a brain signal related to planning and execution of motor tasks. From an MRCP, three notable features can be identified: the early Bereitschaftspotential (BP1), the late Bereitschaftspotential (BP2), and the negative peak (PN). These features have been used in past studies to quantify neurophysiological changes in response to motor training. Currently, either manual labeling or a priori specification of time points is used to extract these features. The limitation of these methods is the inability to fully model the features. This paper proposes the segmented regression along with a local peak method for automated labeling of the features. The proposed method derives the onsets, amplitudes at onsets, and slopes of BP1 and BP2 along with time and amplitude of the PN in a typical average MRCP. To choose the most suitable regression technique a bounded segmented regression method, a change point method and multivariate adaptive regression splines were evaluated using the root-mean-square error on a dataset of 6000 simulated MRCPs. The best-performing regression technique combined with the local peak method was then applied to a smaller set of 123 simulated MRCPs. Error in onsets of BP1 and BP2 and time of PN were compared with the errors in manual labeling by an expert. The performance of the proposed method was also evaluated on an experimental dataset of MRCPs derived from electroencephalography (EEG) recorded across two sessions from 22 healthy participants during a lower limb task. The Bland-Altman plots were used to evaluate the absolute reliability of the proposed method. On experimental data, the proposed method was also compared with manual labeling by an expert. Bounded segmented regression produced the smallest error on the simulation data. For the experimental data, our proposed method did not exhibit statistically significant bias in any of the modeled features. Furthermore, its performance was comparable to manual labeling by experts. We conclude that the proposed method can be used to automatically obtain robust estimates for the MRCP features with known measurement error.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.