Joseph T. Gwin

Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

Electrocortical activity is coupled to gait cycle phase during treadmill walking

Recent findings suggest that human cortex is more active during steady-speed unperturbed locomotion than previously thought. However, techniques that have been used to image the brain during locomotion lack the temporal resolution necessary to assess intra-stride cortical dynamics. Our aim was to determine if electrocortical activity is coupled to gait cycle phase during steady-speed human walking. We used electroencephalography (EEG), motion capture, and a force-measuring treadmill to record brain and body dynamics while eight healthy young adult subjects walked on a treadmill. Infomax independent component analysis (ICA) parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach, and clustered electrocortical sources across subjects by similarities in dipole locations and power spectra. We then computed spectrograms for each electrocortical source that were time-locked to the gait cycle. Electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex exhibited significant (p<0.05) intra-stride changes in spectral power. During the end of stance, as the leading foot was contacting the ground and the trailing foot was pushing off, alpha- and beta-band spectral power increased in or near the left/right sensorimotor and dorsal anterior cingulate cortex. Power increases in the left/right sensorimotor cortex were more pronounced for contralateral limb push-off (ipsilateral heel-strike) than for ipsilateral limb push-off (contralateral heel-strike). Intra-stride high-gamma spectral power changes were evident in anterior cingulate, posterior parietal, and sensorimotor cortex. These data confirm cortical involvement in steady-speed human locomotion. Future applications of these techniques could provide critical insight into the neural mechanisms of movement disorders and gait rehabilitation.

Visual evoked responses during standing and walking.

Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others’ motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.

Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response

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 with 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 with 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.

Imaging natural cognition in action

The primary function of the human brain is arguably to optimize the results of our motor actions in an ever-changing environment. Our cognitive processes and supporting brain dynamics are inherently coupled both to our environment and to our physical structure and actions. To investigate human cognition in its most natural forms demands imaging of brain activity while participants perform naturally motivated actions and interactions within a full three-dimensional environment. Transient, distributed brain activity patterns supporting spontaneous motor actions, performed in pursuit of naturally motivated goals, may involve any or all parts of cortex and must be precisely timed at a speed faster than the speed of thought and action. Hemodynamic imaging methods give information about brain dynamics on a much slower scale, and established techniques for imaging brain dynamics in all modalities forbid participants from making natural extensive movements so as to avoid intractable movement-related artifacts. To overcome these limitations, we are developing mobile brain/body imaging (MoBI) approaches to study natural human cognition. By synchronizing lightweight, high-density electroencephalographic (EEG) recording with recordings of participant sensory experience, body and eye movements, and other physiological measures, we can apply advanced data analysis techniques to the recorded signal ensemble. This MoBI approach enables the study of human brain dynamics accompanying active human cognition in its most natural forms. Results from our studies have provided new insights into the brain dynamics supporting natural cognition and can extend theories of human cognition and its evolutionary function - to optimize the results of our behavior to meet ever-changing goals, challenges, and opportunities.

Beta- and gamma-range human lower limb corticomuscular coherence

Coherence between electroencephalography (EEG) recorded on the scalp above the motor cortex and electromyography (EMG) recorded on the skin of the limbs is thought to reflect corticospinal coupling between motor cortex and muscle motor units. Beta-range (13–30 Hz) corticomuscular coherence has been extensively documented during static force output while gamma-range (31–45 Hz) coherence has been linked to dynamic force output. However, the explanation for this beta-to-gamma coherence shift remains unclear. We recorded 264-channel EEG and 8-channel lower limb EMG while eight healthy subjects performed isometric and isotonic, knee, and ankle exercises. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We computed magnitude squared coherence between electrocortical source signals and EMG. Significant coherence between contralateral motor cortex electrocortical signals and lower limb EMG was observed in the beta- and gamma-range for all exercise types. Gamma-range coherence was significantly greater for isotonic exercises than for isometric exercises. We conclude that active muscle movement modulates the speed of corticospinal oscillations. Specifically, isotonic contractions shift corticospinal oscillations toward the gamma-range while isometric contractions favor beta-range oscillations. Prior research has suggested that tasks requiring increased integration of visual and somatosensory information may shift corticomuscular coherence to the gamma-range. The isometric and isotonic tasks studied here likely required similar amounts of visual and somatosensory integration. This suggests that muscle dynamics, including the amount and type of proprioception, may play a role in the beta-to-gamma shift.

An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions

BackgroundElectroencephalography (EEG) combined with independent component analysis enables functional neuroimaging in dynamic environments including during human locomotion. This type of functional neuroimaging could be a powerful tool for neurological rehabilitation. It could enable clinicians to monitor changes in motor control related cortical dynamics associated with a therapeutic intervention, and it could facilitate noninvasive electrocortical control of devices for assisting limb movement to stimulate activity dependent plasticity. Understanding the relationship between electrocortical dynamics and muscle activity will be helpful for incorporating EEG-based functional neuroimaging into clinical practice. The goal of this study was to use independent component analysis of high-density EEG to test whether we could relate electrocortical dynamics to lower limb muscle activation in a constrained motor task. A secondary goal was to assess the trial-by-trial consistency of the electrocortical dynamics by decoding the type of muscle action.MethodsWe recorded 264-channel EEG while 8 neurologically intact subjects performed isometric and isotonic, knee and ankle exercises at two different effort levels. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We generated spectrograms for all electrocortical source signals and used a naïve Bayesian classifier to decode exercise type from trial-by-trial time-frequency data.ResultsAMICA captured different electrocortical source distributions for ankle and knee tasks. The fit of single-trial EEG to these models distinguished knee from ankle tasks with 80% accuracy. Electrocortical spectral modulations in the supplementary motor area were significantly different for isometric and isotonic tasks (p < 0.05). Isometric contractions elicited an event related desynchronization (ERD) in the α-band (8–12 Hz) and β-band (12–30 Hz) at joint torque onset and offset. Isotonic contractions elicited a sustained α- and β-band ERD throughout the trial. Classifiers based on supplementary motor area sources achieved a 4-way classification accuracy of 69% while classifiers based on electrocortical sources in multiple brain regions achieved a 4-way classification accuracy of 87%.ConclusionsIndependent component analysis of EEG reveals unique spatial and spectro-temporal electrocortical properties for different lower limb motor tasks. Using a broad distribution of electrocortical signals may improve classification of human lower limb movements from single-trial EEG.

Weighted phase lag index stability as an artifact resistant measure to detect cognitive EEG activity during locomotion

BackgroundHigh-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.MethodsWe 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.ResultsApplying 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.ConclusionsThese 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.

An Investigation of the NOCSAE Linear Impactor Test Method Based on In Vivo Measures of Head Impact Acceleration in American Football

The performance characteristics of football helmets are currently evaluated by simulating head impacts in the laboratory using a linear drop test method. To encourage development of helmets designed to protect against concussion, the National Operating Committee for Standards in Athletic Equipment recently proposed a new headgear testing methodology with the goal of more closely simulating in vivo head impacts. This proposed test methodology involves an impactor striking a helmeted headform, which is attached to a nonrigid neck. The purpose of the present study was to compare headform accelerations recorded according to the current (n=30) and proposed (n=54) laboratory test methodologies to head accelerations recorded in the field during play. In-helmet systems of six single-axis accelerometers were worn by the Dartmouth College mens football team during the 2005 and 2006 seasons (n=20,733 impacts; 40 players). The impulse response characteristics of a subset of laboratory test impacts (n=27) were compared with the impulse response characteristics of a matched sample of in vivo head accelerations (n=24). Second- and third-order underdamped, conventional, continuous-time process models were developed for each impact. These models were used to characterize the linear head/headform accelerations for each impact based on frequency domain parameters. Headform linear accelerations generated according to the proposed test method were less similar to in vivo head accelerations than headform accelerations generated by the current linear drop test method. The nonrigid neck currently utilized was not developed to simulate sport-related direct head impacts and appears to be a source of the discrepancy between frequency characteristics of in vivo and laboratory head/headform accelerations. In vivo impacts occurred 37% more frequently on helmet regions, which are tested in the proposed standard than on helmet regions tested currently. This increase was largely due to the addition of the facemask test location. For the proposed standard, impactor velocities as high as 10.5 m/s were needed to simulate the highest energy impacts recorded in vivo. The knowledge gained from this study may provide the basis for improving sports headgear test apparatuses with regard to mimicking in vivo linear head accelerations. Specifically, increasing the stiffness of the neck is recommended. In addition, this study may provide a basis for selecting appropriate test impact energies for the standard performance specification to accompany the proposed standard linear impactor test method.

Walking reduces sensorimotor network connectivity compared to standing

BackgroundConsiderable effort has been devoted to mapping the functional and effective connectivity of the human brain, but these efforts have largely been limited to tasks involving stationary subjects. Recent advances with high-density electroencephalography (EEG) and Independent Components Analysis (ICA) have enabled study of electrocortical activity during human locomotion. The goal of this work was to measure the effective connectivity of cortical activity during human standing and walking.MethodsWe recorded 248-channels of EEG as eight young healthy subjects stood and walked on a treadmill both while performing a visual oddball discrimination task and not performing the task. ICA parsed underlying electrocortical, electromyographic, and artifact sources from the EEG signals. Inverse source modeling methods and clustering algorithms localized posterior, anterior, prefrontal, left sensorimotor, and right sensorimotor clusters of electrocortical sources across subjects. We applied a directional measure of connectivity, conditional Granger causality, to determine the effective connectivity between electrocortical sources.ResultsConnections involving sensorimotor clusters were weaker for walking than standing regardless of whether the subject was performing the simultaneous cognitive task or not. This finding supports the idea that cortical involvement during standing is greater than during walking, possibly because spinal neural networks play a greater role in locomotor control than standing control. Conversely, effective connectivity involving non-sensorimotor areas was stronger for walking than standing when subjects were engaged in the simultaneous cognitive task.ConclusionsOur results suggest that standing results in greater functional connectivity between sensorimotor cortical areas than walking does. Greater cognitive attention to standing posture than to walking control could be one interpretation of that finding. These techniques could be applied to clinical populations during gait to better investigate neural substrates involved in mobility disorders.