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EEG Cortical Neuroimaging during Human Full-Body Movement.
Abstract
Studying how the human brain functions during full-body movement can increase our understanding of how to diagnose and treat neurological disorders. High-density electroencephalography (EEG) can record brain activity during body movement due to its portability and excellent time resolution. However, EEG is prone to movement artifact, and traditional EEG methods have poor spatial resolution. Combining EEG with independent component analysis (ICA) and inverse source modeling can improve spatial resolution. In my first study, I used EEG and ICA to investigate the biomechanical and neural interplay of performing a complicated cognitive task at different walking speeds. Young, healthy subjects stepped significantly wider when walking with the cognitive task compared to walking alone, but walking speed did not affect cognitive performance (i.e. reaction time and correct responses). EEG results mirrored cognitive performance, in that there were similar event-related desynchronizations in the somatosensory association cortex around encoding at all speeds. For my second study, I addressed the problem of movement artifact in EEG. I created an interface that blocked true electrocortical signals while recording only movement artifact. I quantified the spectral changes in the movement artifact EEG, tested various methods of removing the artifact, and compared their efficacies. Artifact spectral power varied across individuals, electrode locations, and walking speed. None of the cleaning methods removed all artifact. For my third study, I examined cortical spectral power fluctuations and effective connectivity during active and viewed full-body exercise with different combinations of arm and leg effort. Larger spectral fluctuations occurred in the cortex during rhythmic arm exercise compared to rhythmic leg exercise, which suggests that rhythmic arm movement is more cortically driven. The strength and direction of information flow was very similar between the active and viewed exercise conditions, with the right motor cortex being the hub of information flow. These studies provide insight into how the human brain functions during full-body movement and may have applications for rehabilitation after a brain injury or in brain monitoring for improving cognitive performance.
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In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation.
Objective. High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. Approach. We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s⁻¹. We then tested artifact removal methods including moving average and wavelet-based techniques. Main results. Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Significance. Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.
The pervasive nature of handedness across human history and cultures is a salient consequence of brain lateralization. This paper presents evidence that provides a structure for understanding the motor control processes that give rise to handedness. According to the Dynamic Dominance Model, the left hemisphere (in right handers) is proficient for processes that predict the effects of body and environmental dynamics, while the right hemisphere is proficient at impedance control processes that can minimize potential errors when faced with unexpected mechanical conditions, and can achieve accurate steady-state positions. This model can be viewed as a motor component for the paradigm of brain lateralization that has been proposed by Rogers et al. (MacNeilage et al., 2009) that is based upon evidence from a wide range of behaviors across many vertebrate species. Rogers proposed a left-hemisphere specialization for well-established patterns of behavior performed in familiar environmental conditions, and a right hemisphere specialization for responding to unforeseen environmental events. The dynamic dominance hypothesis provides a framework for understanding the biology of motor lateralization that is consistent with Roger's paradigm of brain lateralization.
Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG) recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source modeling to high-density (120 channels) EEG recordings using individual anatomy reconstructed from structural magnetic resonance imaging scans. First, we analyzed amplitude differences between the conditions, walking and upright standing. Second, we investigated amplitude modulations related to the gait phase. During active walking upper μ (10–12 Hz) and β (18–30 Hz) oscillations were suppressed [event-related desynchronization (ERD)] compared to upright standing. Significant β ERD activity was located focally in central sensorimotor areas for 9/10 subjects. Additionally, we found that low γ (24–40 Hz) amplitudes were modulated related to the gait phase. Because there is a certain frequency band overlap between sustained β ERD and gait phase related modulations in the low γ range, these two phenomena are superimposed. Thus, we observe gait phase related amplitude modulations at a certain ERD level. We conclude that sustained μ and β ERD reflect a movement related state change of cortical excitability while gait phase related modulations in the low γ represent the motion sequence timing during gait. Interestingly, the center frequencies of sustained β ERD and gait phase modulated amplitudes were identified to be different. They may therefore be caused by different neuronal rhythms, which should be taken under consideration in future studies.
When humans walk in everyday life, they typically perform a range of cognitive tasks while they are on the move. Past studies examining performance changes in dual cognitive-motor tasks during walking have produced a variety of results. These discrepancies may be related to the type of cognitive task chosen, differences in the walking speeds studied, or lack of controlling for walking speed. The goal of this study was to determine how young, healthy subjects performed a spatial working memory task over a range of walking speeds. We used high-density electroencephalography to determine if electrocortical activity mirrored changes in cognitive performance across speeds. Subjects stood (0.0 m/s) and walked (0.4, 0.8, 1.2, and 1.6 m/s) with and without performing a Brooks spatial working memory task. We hypothesized that performance of the spatial working memory task and the associated electrocortical activity would decrease significantly with walking speed. Across speeds, the spatial working memory task caused subjects to step more widely compared with walking without the task. This is typically a sign that humans are adapting their gait dynamics to increase gait stability. Several cortical areas exhibited power fluctuations time-locked to memory encoding during the cognitive task. In the somatosensory association cortex, alpha power increased prior to stimulus presentation and decreased during memory encoding. There were small significant reductions in theta power in the right superior parietal lobule and the posterior cingulate cortex around memory encoding. However, the subjects did not show a significant change in cognitive task performance or electrocortical activity with walking speed. These findings indicate that in young, healthy subjects walking speed does not affect performance of a spatial working memory task. These subjects can devote adequate cortical resources to spatial cognition when needed, regardless of walking speed.
This review focuses on a novel rehabilitation approach known as action observation treatment (AOT). It is now a well-accepted notion in neurophysiology that the observation of actions performed by others activates in the perceiver the same neural structures responsible for the actual execution of those same actions. Areas endowed with this action observation-action execution matching mechanism are defined as the mirror neuron system. AOT exploits this neurophysiological mechanism for the recovery of motor impairment. During one typical session, patients observe a daily action and afterwards execute it in context. So far, this approach has been successfully applied in the rehabilitation of upper limb motor functions in chronic stroke patients, in motor recovery of Parkinson's disease patients, including those presenting with freezing of gait, and in children with cerebral palsy. Interestingly, this approach also improved lower limb motor functions in post-surgical orthopaedic patients. AOT is well grounded in basic neuroscience, thus representing a valid model of translational medicine in the field of neurorehabilitation. Moreover, the results concerning its effectiveness have been collected in randomized controlled studies, thus being an example of evidence-based clinical practice.
Voluntary drive is crucial for motor learning, therefore we are interested in the role that motor planning plays in gait movements. In this study we examined the impact of an interactive Virtual Environment (VE) feedback task on the EEG patterns during robot assisted walking. We compared walking in the VE modality to two control conditions: walking with a visual attention paradigm, in which visual stimuli were unrelated to the motor task; and walking with mirror feedback, in which participants observed their own movements. Eleven healthy participants were considered. Application of independent component analysis to the EEG revealed three independent component clusters in premotor and parietal areas showing increased activity during walking with the adaptive VE training paradigm compared to the control conditions. During the interactive VE walking task spectral power in frequency ranges 8–12, 15–20, and 23–40 Hz was significantly (p ≤ 0.05) decreased. This power decrease is interpreted as a correlate of an active cortical area. Furthermore activity in the premotor cortex revealed gait cycle related modulations significantly different (p ≤ 0.05) from baseline in the frequency range 23–40 Hz during walking. These modulations were significantly (p ≤ 0.05) reduced depending on gait cycle phases in the interactive VE walking task compared to the control conditions. We demonstrate that premotor and parietal areas show increased activity during walking with the adaptive VE training paradigm, when compared to walking with mirror- and movement unrelated feedback. Previous research has related a premotor-parietal network to motor planning and motor intention. We argue that movement related interactive feedback enhances motor planning and motor intention. We hypothesize that this might improve gait recovery during rehabilitation.
In the current study we sought to dissociate the component processes of working memory (WM) (vigilance, encoding and maintenance) that may be differentially impaired in attention-deficit/ hyperactivity disorder (ADHD). We collected electroencephalographic (EEG) data from 52 children with ADHD and 47 typically developing (TD) children, ages 7-14 years, while they performed a spatial Sternberg working memory task. We used independent component analysis and time-frequency analysis to identify midoccipital alpha (8-12 Hz) to evaluate encoding processes and frontal midline theta (4-7 Hz) to evaluate maintenance processes. We tested for effects of task difficulty and cue processing to evaluate vigilance. Children with ADHD showed attenuated alpha band event-related desynchronization (ERD) during encoding. This effect was more pronounced when task difficulty was low (consistent with impaired vigilance) and was predictive of memory task performance and symptom severity. Correlated with alpha ERD during encoding were alpha power increases during the maintenance period (relative to baseline), suggesting a compensatory effort. Consistent with this interpretation, midfrontal theta power increases during maintenance were stronger in ADHD and in high-load memory conditions. Furthermore, children with ADHD exhibited a maturational lag in development of posterior alpha power whereas age-related changes in frontal theta power deviated from the TD pattern. Last, subjects with ADHD showed age-independent attenuation of evoked responses to warning cues, suggesting low vigilance. Combined, these three EEG measures predicted diagnosis with 70% accuracy. We conclude that the interplay of impaired vigilance and encoding in ADHD may compromise maintenance and lead to impaired WM performance in this group.
We aimed to determine the effect of distinctly different cognitive tasks and walking speed on cognitive motor interference of dual-task (DT) walking.
Fifteen healthy adults performed four cognitive tasks: visuomotor reaction time (VMRT) task, word list generation (WLG) task, serial subtraction (SS) task, and the Stroop (STR) while sitting and at preferred-speed (dual-task normal walking) and slow-speed (dual-task slow) walking conditions. Gait speed was recorded to determine effect on walking. Motor and cognitive costs were measured.
Dual-task walking had a significant effect on motor and cognitive parameters. At preferred-speed, motor cost was lowest for the VMRT task and highest for the STR task. In contrast, the cognitive cost was highest for the VMRT task and lowest for the STR task. Dual-task slow walking resulted in increased motor cost and decreased cognitive cost only for the STR.
Results show the motor and cognitive cost of dual-task walking depends heavily on the type and perceived complexity of cognitive task being performed. Cognitive costs for the STR task were low irrespective of walking speed, suggesting that at preferred-speed individuals prioritize complex cognitive tasks requiring higher attentional and processing resources over the walking. While performing VMRT task, individuals preferred to prioritize more complex walking task over VMRT task resulting in lesser motor cost and increased cognitive cost for VMRT task. Furthermore, slow walking can assist in diverting greater attention towards complex cognitive tasks improving its performance while walking.
Thirty one young (mean age = 20.8 years) and 30 older (mean age = 71.5 years) men and women categorized as physically active (n = 31) or inactive (n = 30) performed an executive processing task while standing, treadmill walking at a preferred pace, and at a faster pace. Dual-task interference was predicted to negatively impact older adults' cognitive flexibility as measured by an auditory switch task more than younger adults; further, participants' level of physical activity was predicted to mitigate the relation. For older adults, treadmill walking was accompanied by significantly more rapid response times and reductions in local- and mixed-switch costs. A speed-accuracy tradeoff was observed in which response errors increase linearly as walking speed increased, suggesting that locomotion under dual-task conditions degrades the quality of older adults' cognitive flexibility. Participants' level of physical activity did not influence cognitive test performance.
Gait performance exhibits patterns within the stride-to-stride variability that can be indexed using detrended fluctuation analysis (DFA). Previous work employing DFA has shown that gait patterns can be influenced by constraints, such as natural aging or disease, and they are informative regarding a person's functional ability. Many activities of daily living require concurrent performance in the cognitive and gait domains; specifically working memory is commonly engaged while walking, which is considered dual-tasking. It is unknown if taxing working memory while walking influences gait performance as assessed by DFA. This study used a dual-tasking paradigm to determine if performance decrements are observed in gait or working memory when performed concurrently. Healthy young participants (N = 16) performed a working memory task (automated operation span task) and a gait task (walking at a self-selected speed on a treadmill) in single- and dual-task conditions. A second dual-task condition (reading while walking) was included to control for visual attention, but also introduced a task that taxed working memory over the long term. All trials involving gait lasted at least 10 min. Performance in the working memory task was indexed using five dependent variables (absolute score, partial score, speed error, accuracy error, and math error), while gait performance was indexed by quantifying the mean, standard deviation, and DFA α of the stride interval time series. Two multivariate analyses of variance (one for gait and one for working memory) were used to examine performance in the single- and dual-task conditions. No differences were observed in any of the gait or working memory dependent variables as a function of task condition. The results suggest the locomotor system is adaptive enough to complete a working memory task without compromising gait performance when walking at a self-selected pace.
This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.
Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp, and to evaluate the degree of artefact attenuation that can be achieved via Reference Layer Artefact Subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, whilst allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.
Determining the neural correlates of loss of balance during walking could lead to improved clinical assessment and treatment for individuals predisposed to falls. We used high-density electroencephalography (EEG) combined with independent component analysis (ICA) to study loss of balance during human walking. We examined 26 healthy young subjects performing heel-to-toe walking on a treadmill-mounted balance beam as well as walking on the treadmill belt (both at 0.22 m/s). ICA identified clusters of electrocortical EEG sources located in or near anterior cingulate, anterior parietal, superior dorsolateral prefrontal and medial sensorimotor cortex that exhibited significantly larger mean spectral power in the theta band (4-7 Hz) during walking on the balance beam compared to treadmill walking. Left and right sensorimotor cortex clusters produced significantly less power in the beta band (12-30 Hz) during walking on the balance beam compared to treadmill walking. For each source cluster, we also computed a normalized mean time/frequency spectrogram time locked to the gait cycle during loss of balance (i.e., when subjects stepped off the balance beam). All clusters except the medial sensorimotor cluster exhibited a transient increase in theta band power during loss of balance. Cluster spectrograms demonstrated that the first electrocortical indication of impending loss of balance occurred in the left sensorimotor cortex at the transition from single support to double support prior to stepping off the beam. These findings provide new insight into the neural correlates of walking balance control and could aid future studies on elderly individuals and others with balance impairments.
Background:
The purpose of this study was to examine the effects of walking at self-selected speed on an active workstation on cognitive performance.
Methods:
Sixty-six participants (n = 27 males, 39 females; mean age = 21.06 ± 1.6 years) completed a treadmill-desk walking and a seated control condition, separated by 48 hours. During each condition, participants completed computerized versions of the Stroop test, a modified flanker task, and a test of reading comprehension.
Results:
No significant differences in response speed or accuracy were found between walking and sitting conditions for any the cognitive tests.
Conclusions:
These findings reveal that performance on cognitive tasks, including executive control processes, are not impaired by walking on an active workstation. Implementing active workstations into offices and classrooms may help to decrease sedentariness without impairing task performance.
Mobility limitations and cognitive impairments, each common with aging, reduce levels of physical and mental activity, are prognostic of future adverse health events, and are associated with an increased fall risk. The purpose of this study was to examine whether divided attention during walking at a constant speed would decrease locomotor rhythm, stability, and cognitive performance. Young healthy participants (n=20) performed a visuo-spatial cognitive task in sitting and while treadmill walking at 2 speeds (0.7 and 1.0m/s).Treadmill speed had a significant effect on temporal gait variables and ML-COP excursion. Cognitive load did not have a significant effect on average temporal gait variables or COP excursion, but variation of gait variables increased during dual-task walking. ML and AP trunk motion was found to decrease during dual-task walking. There was a significant decrease in cognitive performance (success rate, response time and movement time) while walking, but no effect due to treadmill speed. In conclusion walking speed is an important variable to be controlled in studies that are designed to examine effects of concurrent cognitive tasks on locomotor rhythm, pacing and stability. Divided attention during walking at a constant speed did result in decreased performance of a visuo-spatial cognitive task and an increased variability in locomotor rhythm.
Brain Computer Interfaces could be useful in rehabilitation of movement, perhaps also for gait. Until recently, research on movement related brain signals has not included measuring electroencephalography (EEG) during walking, because of the potential artifacts. We investigated if it is possible to measure the event Related Desynchronization (ERD) and event related spectral perturbations (ERSP) during walking. Six subjects walked on a treadmill with a slow speed, while EEG, electromyography (EMG) of the neck muscles and step cycle were measured. A Canonical Correlation Analysis (CCA) was used to remove EMG artifacts from the EEG signals. It was shown that this method correctly deleted EMG components. A strong ERD in the mu band and a somewhat less strong ERD in the beta band were found during walking compared to a baseline period. Furthermore, lateralized ERSPs were found, depending on the phase in the step cycle. It is concluded that this is a promising method to use in BCI research on walking. These results therefore pave the way for using brain signals related to walking in a BCI context.
Gait impairments are prevalent among people with Parkinson's disease (PD). Instructions to focus on walking can improve walking in PD, but the use of such a cognitive strategy may be limited under dual-task walking conditions, when walking is performed simultaneously with concurrent cognitive or motor tasks. This study examined how dual-task performance of walking and a concurrent cognitive task was affected by instructions in people with PD compared to healthy young and older individuals. Dual-task walking and cognitive task performance was characterized under two sets of instructions as follows: (1) focus on walking and (2) focus on the cognitive task. People with PD and healthy adults walked faster when instructed to focus on walking. However, when focused on walking, people with PD and young adults demonstrated declines in the cognitive task. This suggests that dual-task performance is flexible and can be modified by instructions in people with PD, but walking improvements may come at a cost to cognitive task performance. The ability to modify dual-task performance in response to instructions or other task and environmental factors is critical to mobility in daily life. Future research should continue to examine factors that influence dual-task performance among people with PD.
To test the hypothesis that neural oscillations synchronize to mediate memory encoding, we analyzed electrocorticographic recordings taken as 68 human neurosurgical patients studied and subsequently recalled lists of common words. To the extent that changes in spectral power reflect synchronous oscillations, we would expect those power changes to be accompanied by increases in phase synchrony between the region of interest and neighboring brain areas. Contrary to the hypothesized role of synchronous gamma oscillations in memory formation, we found that many key regions that showed power increases during successful memory encoding also exhibited decreases in global synchrony. Similarly, cortical theta activity that decreases during memory encoding exhibits both increased and decreased global synchrony depending on region and stage of encoding. We suggest that network synchrony analyses, as used here, can help to distinguish between two major types of spectral modulations: (1) those that reflect synchronous engagement of regional neurons with neighboring brain areas, and (2) those that reflect either asynchronous modulations of neural activity or local synchrony accompanied by global disengagement from neighboring regions. We show that these two kinds of spectral modulations have distinct spatiotemporal profiles during memory encoding.
It has been proposed that working memory (WM) is updated/manipulated via a fronto-basal-ganglia circuit. One way that this could happen is via the synchronization of neural oscillations. A first step toward testing this hypothesis is to clearly establish a frontal scalp EEG signature of WM manipulation. Although many EEG studies have indeed revealed frontal EEG signatures for WM, especially in the theta frequency band (3-8 Hz), few of them required subjects to manipulate WM, and of those that did, none specifically tied the EEG signature to the manipulation process per se. Here we employed a WM manipulation task that has been shown with imaging to engage the prefrontal cortex and the striatum. We adapted this task to titrate the success of WM manipulation to approximately 50 %. Using time-frequency analysis of EEG, we showed that theta power increased over frontal cortex for successful versus failed WM manipulation, specifically at the time of the manipulation event. This establishes a clear-cut EEG signature of WM manipulation. Future studies could employ this to test the fronto-basal-ganglia hypothesis of WM updating/manipulation.
Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are however considerably fewer techniques available if only a single channel measurement is available and yet single channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single channel measurements. The EEMD technique is first used to decompose the single channel signal into a multi-dimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second order statistics. The new technique is tested against the currently available Wavelet denoising and EEMDICA techniques using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data and is shown to produce significantly improved results.
Background
High-density electroencephalography (EEG) with active electrodes allows for monitoring of electrocortical dynamics during human walking but movement artifacts have the potential to dominate the signal. One potential method for recovering cognitive brain dynamics in the presence of gait-related artifact is the Weighted Phase Lag Index.
Methods
We tested the ability of Weighted Phase Lag Index to recover event-related potentials during locomotion. Weighted Phase Lag Index is a functional connectivity measure that quantified how consistently 90° (or 270°) phase ‘lagging’ one EEG signal was compared to another. 248-channel EEG was recorded as eight subjects performed a visual oddball discrimination and response task during standing and walking (0.8 or 1.2 m/s) on a treadmill.
Results
Applying Weighted Phase Lag Index across channels we were able to recover a p300-like cognitive response during walking. This response was similar to the classic amplitude-based p300 we also recovered during standing. We also showed that the Weighted Phase Lag Index detects more complex and variable activity patterns than traditional voltage-amplitude measures. This variability makes it challenging to compare brain activity over time and across subjects. In contrast, a statistical metric of the index’s variability, calculated over a moving time window, provided a more generalized measure of behavior. Weighted Phase Lag Index Stability returned a peak change of 1.8% + −0.5% from baseline for the walking case and 3.9% + −1.3% for the standing case.
Conclusions
These findings suggest that both Weighted Phase Lag Index and Weighted Phase Lag Index Stability have potential for the on-line analysis of cognitive dynamics within EEG during human movement. The latter may be more useful from extracting general principles of neural behavior across subjects and conditions.
The observation of action done by others determines a desynchronization of the rhythms recorded from cortical central regions. Here, we examined whether the observation of different types of hand movements (target directed, non-target directed, cyclic and non-cyclic) elicits different EEG cortical temporal patterns.
Video-clips of four types of hand movements were shown to right-handed healthy participants. Two were target directed (grasping and pointing) motor acts; two were non-target directed (supinating and clenching) movements. Grasping and supinating were performed once, while pointing and clenching twice (cyclic movements). High-density EEG was recorded and analyzed by means of wavelet transform, subdividing the time course in time bins of 200 ms. The observation of all presented movements produced a desynchronization of alpha and beta rhythms in central and parietal regions. The rhythms desynchronized as soon as the hand movement started, the nadir being reached around 700 ms after movement onset. At the end of the movement, a large power rebound occurred for all bands. Target and non-target directed movements produced an alpha band desynchronization in the central electrodes at the same time, but with a stronger desynchronization and a prolonged rebound for target directed motor acts. Most interestingly, there was a clear correlation between the velocity profile of the observed movements and beta band modulation.
Our data show that the observation of motor acts determines a modulation of cortical rhythm analogous to that occurring during motor act execution. In particular, the cortical motor system closely follows the velocity of the observed movements. This finding provides strong evidence for the presence in humans of a mechanism (mirror mechanism) mapping action observation on action execution motor programs.
Brain activity is associated with changes in optical properties of brain tissue. Optical measurements during brain activation can assess haemoglobin oxygenation, cytochrome-c-oxidase redox state, and two types of changes in light scattering reflecting either membrane potential (fast signal) or cell swelling (slow signal), respectively. In previous studies of exposed brain tissue, optical imaging of brain activity has been achieved at high temporal and microscopical spatial resolution. Now, using near-infrared light that can penetrate biological tissue reasonably well, it has become possible to assess brain activity in human subjects through the intact skull non-invasively. After early studies employing single-site near-infrared spectroscopy, first near-infrared imaging devices are being applied successfully for low-resolution functional brain imaging. Advantages of the optical methods include biochemical specificity, a temporal resolution in the millisecond range, the potential of measuring intracellular and intravascular events simultaneously and the portability of the devices enabling bedside examinations.
Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
Anterior cingulate cortex (ACC) is a part of the brain's limbic system. Classically, this region has been related to affect, on the basis of lesion studies in humans and in animals. In the late 1980s, neuroimaging research indicated that ACC was active in many studies of cognition. The findings from EEG studies of a focal area of negativity in scalp electrodes following an error response led to the idea that ACC might be the brain's error detection and correction device. In this article, these various findings are reviewed in relation to the idea that ACC is a part of a circuit involved in a form of attention that serves to regulate both cognitive and emotional processing. Neuroimaging studies showing that separate areas of ACC are involved in cognition and emotion are discussed and related to results showing that the error negativity is influenced by affect and motivation. In addition, the development of the emotional and cognitive roles of ACC are discussed, and how the success of this regulation in controlling responses might be correlated with cingulate size. Finally, some theories are considered about how the different subdivisions of ACC might interact with other cortical structures as a part of the circuits involved in the regulation of mental and emotional activity.
Although using a treadmill workstation may change the sedentary nature of desk jobs, it is unknown if walking while working affects performance on office-work related tasks. Purpose: To assess differences between seated and walking conditions on motor skills and cognitive function tests. Methods: Eleven males (24.6 ± 3.5 y) and 9 females (27.0 ± 3.9 y) completed a test battery to assess selective attention and processing speed, typing speed, mouse clicking/drag-and-drop speed, and GRE math and reading comprehension. Testing was performed under seated and walking conditions on 2 separate days using a counterbalanced, within subjects design. Participants did not have an acclimation period before the walking condition. Results: Paired t tests (P < .05) revealed that in the seated condition, completion times were shorter for mouse clicking (26.6 ± 3.0 vs. 28.2 ± 2.5s) and drag-and-drop (40.3 ± 4.2 vs. 43.9 ± 2.5s) tests, typing speed was greater (40.2 ± 9.1 vs. 36.9 ± 10.2 adjusted words · min−1), and math scores were better (71.4 ± 15.2 vs. 64.3 ± 13.4%). There were no significant differences between conditions in selective attention and processing speed or in reading comprehension. Conclusion: Compared with the seated condition, treadmill walking caused a 6% to 11% decrease in measures of fine motor skills and math problem solving, but did not affect selective attention and processing speed or reading comprehension.
The networks of the brainstem and spinal cord that co-ordinate locomotion and body orientation in lamprey are described. The cycle-to-cycle pattern generation of these networks is produced by interacting glutamatergic and glycinergic neurones, with NMDA receptor-channels playing an important role at lower rates of locomotion. The fine tuning of the networks produced by 5-HT, dopamine and GABA systems involves a modulation of Ca2+-dependent K+ channels, high- and low-threshold voltage-activated Ca2+ channels and presynaptic inhibitory mechanisms. Mathematical modelling has been used to explore the capacity of these biological networks. The vestibular control of the body orientation during swimming is exerted via reticulospinal neurones located in different reticular nuclei. These neurones become activated maximally at different angles of tilt.
Objective: Walking is a complex voluntary rhythmic motor behaviour. Its implicit nature suggests that reduced attention resources are required for its execution. The aim of this study was to demonstrate that, to perform a mental calculation while walking, might modify the spatial-temporal parameters of walking in fragile elderly patients. Methods: We compared the walking, in a straight line over a distance of 10 meters, of 30 fragile elderly subjects (mean age 82.6 ± 7.1 years) with that of 30 healthy controls (mean age 37.5 ± 11.5 years). Two walking conditions were studied: with and without a counting task. The time, number of steps, lateral deviations and stops were recorded on a video camera. Results: The condition of a double-task provoked three types of effects on walking: an increase in time and the number of steps in both groups, but significantly greater in the elderly patients than in the control group of patients (6.4 s and 4.6 steps in the elderly versus 0.5 s and 0.4 steps in the controls); a reduction in the cadence and length of the step, only significant in the elderly patients, and a significant increase in the number of lateral deviations and stops in the double-task condition in the elderly patients. Conclusion: Globally, these results indicate that walking requires more attention resources in the elderly than in the middle-aged. The loss of the implicit character of walking to the benefit of cognitive attention resources may partly explain the high risk of falling in fragile elderly subjects.
Investigating human brain function is essential to develop models of cortical involvement during walking. Such models could advance the analysis of motor impairments following brain injuries (e.g. stroke) and may lead to novel rehabilitation approaches. In this work, we applied high-density EEG source imaging based on individual anatomy to enable neuroimaging during walking. To minimize the impact of muscular influence on EEG recordings we introduce a novel artifact correction method based on spectral decomposition. High γ oscillations (>60Hz) were previously reported to play an important role in motor control. Here, we investigate high γ amplitudes while focusing on two different aspects of a walking experiment, namely the fact that a person walks and the rhythmicity of walking. We found high γ amplitudes (60-80Hz) located focally in central sensorimotor areas were significantly increased during walking compared to standing. Moreover, high γ (70-90Hz) amplitudes in the same areas are modulated in relation to the gait cycle. Since the spectral peaks of high γ amplitude increase and modulation do not match, it is plausible that these two high γ elements represent different frequency-specific network interactions. Interestingly, we found high γ (70-90Hz) amplitudes to be coupled to low γ (24-40Hz) amplitudes, which both are modulated in relation to the gait cycle but conversely to each other. In summary, our work is a further step towards modeling cortical involvement during human upright walking.
Copyright © 2015. Published by Elsevier Inc.
In the last years it has become possible to regain some locomotor activity in patients suffering from an incomplete spinal cord injury (SCI) through intense training on a treadmill. The ideas behind this approach owe much to insights derived from animal studies. Many studies showed that cats with complete spinal cord transection can recover locomotor function. These observations were at the basis of the concept of the central pattern generator (CPG) located at spinal level. The evidence for such a spinal CPG in cats and primates (including man) is reviewed in part 1, with special emphasis on some very recent developments which support the view that there is a human spinal CPG for locomotion.
Dual task paradigm states that the introduction of a second task during a cognitive or motor performance results in a decreased performance in either task. Treadmill walk, often used in clinical applications of dual task testing, has never been compared to overground walk, to ascertain its susceptibility to interference from a second task. We compared the effects of overground and treadmill gait on dual task performance.
Gait kinematic parameters and cognitive performance were obtained in 29 healthy older adults (mean age 75years, 14 females) when they were walking freely on a sensorized carpet or during treadmill walking with an optoelectronic system, in single task or dual task conditions, using alternate repetition of letters as a cognitive verbal task.
During overground walking, speed, cadence, step length stride length, and double support time (all with P value<0.001) and cognitive performance (number of correct words, P<0.001) decreased substantially from single to dual task testing. When subjects walked at a fixed speed on the treadmill, cadence decreased significantly (P=0.005), whereas cognitive performance remained unaffected.
Both motor and cognitive performances decline during dual task testing with overground walking. Conversely, cognitive performance remains unaffected in dual task testing on the treadmill. In the light of current dual task paradigm, these findings may have relevant implication for our understanding of motor control, as they suggest that treadmill walk does not involve brain areas susceptible to interference from the introduction of a cognitive task.
Patients with early Parkinson's disease (PD) may not complain of gait difficulties but subtle gait abnormalities may be revealed as part of a "preclinical gait syndrome" when they are challenged by dual tasks. 21 early PD patients (n = 21, mean age 63.5 years, H&Y 1.62, disease duration <5 years, mean UPDRS-III 7.7) who did not have gait complaints were as compared to age- and gender-matched healthy controls (n = 21). Memory function was not different between the two groups. Under normal walking conditions, there were no significant differences in gait parameters between the patients and the control group. In both groups, normalized gait velocity decreased in response to dual tasking in a parallel fashion (p < 0.001). Similarly, gait variability increased in both groups with dual tasking although not statistically significant. In PD patients, the performance of an additional task resulted in an increased number of cadences (p = 0.04), a reduction in swing time (p = 0.02) and cycle time (p = 0.04) compared with the control group but there was no significant reduction in normalized velocity. Stride width also increased in the PD patients. The addition of a cognitive task may affect certain aspects of gait and is able to elicit subclinical deficits in early PD patients. In an attempt to maintain velocity, early PD patients develop compensatory mechanisms by increasing cadence and decreasing swing time and cycle time. Increased step width helps support balance, and prevents going beyond the base-of-support which may predispose to unsteadiness and falls. We propose that these findings occur as part of a spectrum of a "preclinical gait syndrome" and longitudinal studies are needed to assess the predictive values of these early markers of gait deficits.
Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels are successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.
Background:
Dual-task (DT) performance, the ability to divide one's attention between motor and secondary tasks, is required in daily life. Adults with cognitive impairment (CI) experience more difficulty with DTs than healthy older adults, but it is unclear how the degree of CI relates to DT performance, particularly with tasks of varying levels of difficulty.
Purpose:
The purposes of this cross-sectional study were to (1) explore the relationship between cognitive level and DT performance and (2) determine how the difficulty of the combined tasks impacts this relationship.
Methods:
Twenty-three older adults with Mini-Mental State Examination (MMSE) scores ranging from 7 to 30 performed 2 single tasks (ST): the Timed Up and Go (TUG) and a 6-m walk for which self-selected walking speed (SSWS) was calculated. Each ST was repeated under 2 DT conditions: counting forward by 1's (TUG1 and SSWS1) and counting backward by 3's (TUG3 and SSWS3). Dual-task cost (DTC) was calculated for each DT as follows: [(difference between DT and ST motor performance)/ST motor performance] × 100. Spearman rank correlation coefficients were used to determine the relationship between DTC and the MMSE. The Friedman 2-way ANOVA on ranks was used to compare the magnitude of DTC among the 4 DTs.
Results:
Significant correlations between the MMSE and DTC were found for SSWS3, TUG1, and TUG3 (r = 0.43-0.57). SSWS1 had a weaker and nonsignificant correlation between MMSE and DTC (r = 0.36). The TUG3 was the most difficult DT, while the SSWS1 was the easiest DT. All participants, regardless of MMSE score, were able to engage in all DTs.
Discussion and conclusions:
A linear relationship exists between cognition and DTC in older adults with varying cognitive levels. The strength of this relationship is greater for more challenging tasks. We also suggest that patients with CI may be able to engage in more challenging tasks than might be assumed. The impact of task difficulty has implications in the design of future studies of DT training for individuals both with and without CI.
Objective:
This study characterized the brain electrical activity during pedaling, a locomotor-like task, in humans. We postulated that phasic brain activity would be associated with active pedaling, consistent with a cortical role in locomotor tasks.
Methods:
Sixty four channels of electroencephalogram (EEG) and 10 channels of electromyogram (EMG) data were recorded from 10 neurologically-intact volunteers while they performed active and passive (no effort) pedaling on a custom-designed stationary bicycle. Ensemble averaged waveforms, 2 dimensional topographic maps and amplitude of the β (13-35 Hz) frequency band were analyzed and compared between active and passive trials.
Results:
The peak-to-peak amplitude (peak positive-peak negative) of the EEG waveform recorded at the Cz electrode was higher in the passive than the active trials (p < 0.01). β-band oscillations in electrodes overlying the leg representation area of the cortex were significantly desynchronized during active compared to the passive pedaling (p < 0.01). A significant negative correlation was observed between the average EEG waveform for active trials and the composite EMG (summated EMG from both limbs for each muscle) of the rectus femoris (r = -0.77, p < 0.01) the medial hamstrings (r = -0.85, p < 0.01) and the tibialis anterior (r = -0.70, p < 0.01) muscles.
Conclusions:
These results demonstrated that substantial sensorimotor processing occurs in the brain during pedaling in humans. Further, cortical activity seemed to be greatest during recruitment of the muscles critical for transitioning the legs from flexion to extension and vice versa.
Significance:
This is the first study demonstrating the feasibility of EEG recording during pedaling, and owing to similarities between pedaling and bipedal walking, may provide valuable insight into brain activity during locomotion in humans.
Contamination of EEG signals by artefacts arising from head movements has been a serious obstacle in the deployment of automatic neurological event detection systems in ambulatory EEG. In this paper, we present work on categorizing these head-movement artefacts as one distinct class and on using support vector machines to automatically detect their presence. The use of additional physical signals in detecting head-movement artefacts is also investigated by means of support vector machines classifiers implemented with gyroscope waveforms. Finally, the combination of features extracted from EEG and gyroscope signals is explored in order to design an algorithm which incorporates both physical and physiological signals in accurately detecting artefacts arising from head-movements.
In robot assisted gait training, a pattern of human locomotion is executed repetitively with the intention to restore the motor programs associated with walking. Several studies showed that active contribution to the movement is critical for the encoding of motor memory. We propose to use brain monitoring techniques during gait training to encourage active participation in the movement. We investigated the spectral patterns in the electroencephalogram (EEG) that are related to active and passive robot assisted gait. Fourteen healthy participants were considered. Infomax independent component analysis separated the EEG into independent components representing brain, muscle, and eye movement activity, as well as other artifacts. An equivalent current dipole was calculated for each independent component. Independent components were clustered across participants based on their anatomical position and frequency spectra. Four clusters were identified in the sensorimotor cortices that accounted for differences between active and passive walking or showed activity related to the gait cycle. We show that in central midline areas the mu (8-12Hz) and beta (18-21Hz) rhythms are suppressed during active compared to passive walking. These changes are statistically significant: mu (F(1, 13)=11.2 p≤0.01) and beta (F(1, 13)=7.7, p≤0.05). We also show that these differences depend on the gait cycle phases. We provide first evidence of modulations of the gamma rhythm in the band 25 to 40Hz, localized in central midline areas related to the phases of the gait cycle. We observed a trend (F(1, 8)=11.03, p≤0.06) for suppressed low gamma rhythm when comparing active and passive walking. Additionally we found significant suppressions of the mu (F(1, 11)=20.1 p≤0.01), beta (F(1, 11)=11.3 p≤0.05) and gamma (F(1, 11)=4.9 p≤0.05) rhythms near C3 (in the right hand area of the primary motor cortex) during phases of active vs. passive robot assisted walking. To our knowledge this is the first study showing EEG analysis during robot assisted walking. We provide evidence for significant differences in cortical activation between active and passive robot assisted gait. Our findings may help to define appropriate features for single trial detection of active participation in gait training. This work is a further step toward the evaluation of brain monitoring techniques and brain-computer interface technologies for improving gait rehabilitation therapies in a top-down approach.
Until recently, gait was generally viewed as a largely automated motor task, requiring minimal higher-level cognitive input. Increasing evidence, however, links alterations in executive function and attention to gait disturbances. This review discusses the role of executive function and attention in healthy walking and gait disorders while summarizing the relevant, recent literature. We describe the variety of gait disorders that may be associated with different aspects of executive function, and discuss the changes occurring in executive function as a result of aging and disease as well the potential impact of these changes on gait. The attentional demands of gait are often tested using dual tasking methodologies. Relevant studies in healthy adults and patients are presented, as are the possible mechanisms responsible for the deterioration of gait during dual tasking. Lastly, we suggest how assessments of executive function and attention could be applied in the clinical setting as part of the process of identifying and understanding gait disorders and fall risk. © 2007 Movement Disorder Society
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
"Cautious" gait is generally characterized by wider and shorter steps. However, we do not clearly understand the relationship between step characteristics and individuals' stability. Here, we examined the effects of voluntarily altering step width (SW) and step length (SL) on individuals' margins of stability. Fourteen participants completed three 3-min treadmill walking trials during three SL (short, normal with metronome, and long) and three SW (narrow, normal and wide) manipulation conditions. SL manipulations yielded significant changes in mean anterior-posterior (AP) margins of stability (MOS(ap)) (p<0.0005) but not mediolateral (ML) margins of stability (MOS(ml)) (p≥0.0579). Taking wider steps increased mean MOS(ml) while decreasing MOS(ap) (p<0.0005). Walking with either wider or long steps, each of which increases the base of support, yielded increased AP and ML MOS variability (p≤0.0468). Step-to-step analysis of MOS(ml) indicated that subjects took stable steps followed immediately by stable steps. Overall, short-term, voluntary adoption of wider steps may help increase instantaneous lateral stability but shorter steps did not change lateral stability during unperturbed walking. We suggest that the observed changes in stability margins be considered in gait training programs which recommend short-term changes in step characteristics to improve stability.
While we have a fair understanding of how and where forelimb–hand manipulative movements are controlled by the neocortex, due to functional imaging studies, we know little about the control of bipedal movements such as walking because of technical difficulties. We succeeded in visualizing cortical activation patterns of human gait by measuring relative changes in local hemoglobin oxygenation using a recently developed near-infrared spectroscopic (NIRS) topography technique. Walking activities were bilaterally associated with increased levels of oxygenated and total hemoglobin in the medial primary sensorimotor cortices and the supplementary motor areas. Alternating foot movements activated similar but less broad regions. Gait imagery increased activities caudally located in the supplementary motor areas. These findings provide new insight into cortical control of human locomotion. NIRS topography might be also useful for evaluating cerebral activation patterns during pathological gait and rehabilitative intervention.