EEG During Pedaling: Evidence for Cortical Control of Locomotor Tasks

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 \u3c 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 \u3c 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 \u3c 0.01) the medial hamstrings (r = −0.85, p \u3c 0.01) and the tibialis anterior (r = −0.70, p \u3c 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

Induced brain activity as indicator of cognitive processes: experimental-methodical analyses and algorithms for online-applications

Die Signalverarbeitung von elektroenzephalographischen (EEG) Signalen ist ein entscheidendes Werkzeug, um die kognitiven Prozessen verstehen zu können. Beispielweise wird induzierte Hirnaktivität in mehreren Untersuchungen mit kognitiver Leistung assoziiert. Deshalb ist die Gewinnung von elektrophysiologischen Parametern grundlegend für die Charakterisierung von kognitiven Prozessen sowie von kognitiven Dysfunktionen in neurologischen Erkrankungen. Besonders bei Epilepsie treten häufig Störungen wie Gedächtnis-, oder Aufmerksamkeitsprobleme auf, zusätzlich zu Anfällen. Neurofeedback (bzw. EEG-Biofeedback) ist eine Therapiemethode, die zusätzlich zu medikamentösen- und chirurgischen Therapien bei der Behandlung vieler neurologischer Krankheiten, einschließlich Epilepsie, erfolgreich praktiziert wird. Neurofeedback wird jedoch meist dafür angewendet, eine Anfallsreduzierung zu erzielen. Dagegen wird eine Verbesserung kognitiver Fähigkeiten auf der Basis elektrophysiologischer Änderungen selten vorgesehen. Darüber hinaus sind die aktuellen Neurofeedbackstrategien für diesen Zweck ungeeignet. Der Grund dafür sind unter anderem nicht adäquate Verfahren für die Gewinnung und Quantifizierung induzierter Hirnaktivität. Unter Berücksichtigung der oben genannten Punkten wurden die kognitiven Leistungen von einer Patientengruppe (Epilepsie) und einer Probandengruppe anhand der ereignisbezogenen De-/Synchronisation (ERD/ERS) Methode untersucht. Signifikante Unterschiede wurden im Theta bzw. Alpha Band festgestellt. Diese Ergebnisse unterstützen die Verwertung von auf ERD/ERS basierten kognitiven Parametern bei Epilepsie. Anhand einer methodischen Untersuchung von dynamischen Eigenschaften wurde ein onlinefähiger ERD/ERS Algorithmus für zukünftige Neurofeedback Applikationen ausgewählt. Basierend auf dem ausgewählten Parameter wurde eine Methodik für die online Gewinnung und Quantifizierung von kognitionsbezogener induzierter Hirnaktivität entwickelt. Die dazugehörigen Prozeduren sind in Module organisiert, um die Prozessapplikabilität zu erhöhen. Mehrere Bestandteile der Methodik, einschließlich der Rolle von Elektrodenmontagen sowie die Eliminierung bzw. Reduktion der evozierten Aktivität, wurden anhand kognitiver Aufgaben evaluiert und optimiert. Die Entwicklung einer geeigneten Neurofeedback Strategie sowie die Bestätigung der psychophysiologischen Hypothese anhand einer Pilotstudie sollen Gegenstand der zukünftigen Arbeitschritte sein.Processing of electroencephalographic (EEG) signals is a key step towards understanding cognitive brain processes. Particularly, there is growing evidence that the analysis of induced brain oscillations is a powerful tool to analyze cognitive performance. Thus, the extraction of electrophysiological features characterizing not only cognitive processes but also cognitive dysfunctions by neurological diseases is fundamental. Especially in the case of epilepsy, cognitive dysfunctions such as memory or attentional problems are often present additionally to seizures. Neurofeedback (or EEG-biofeedback) is a psychological technique that, as a supplement to medication and surgical therapies, has been demonstrated to provide further improvement in many neurological diseases, including epilepsy. However, most efforts of neurofeedback have traditionally been dedicated to the reduction of seizure frequency, and little attention has been paid for improving cognitive deficits by means of specific electrophysiological changes. Furthermore, current neurofeedback approaches are not suitable for these purposes because the parameters used do not take into consideration the relationship between memory performance and event-induced brain activity. Considering all these aspects, the cognitive performance of a group of epilepsy patients and a group of healthy controls was analyzed based on the event-related de /synchronization (ERD/ERS) method. Significant differences between both populations in the theta and upper alpha bands were observed. These findings support the possible exploitation of cognitive quantitative parameters in epilepsy based on ERD/ERS. An algorithm for the online ERD/ERS calculation was selected for future neurofeedback applications, as the result of a comparative dynamic study. Subsequently, a methodology for the online extraction and quantification of cognitive-induced brain activity was developed based on the selected algorithm. The procedure is functionally organized in blocks of algorithms in order to increase applicability. Several aspects, including the role of electrode montages and the reduction or minimization of the evoked activity, were examined based on cognitive studies as part of the optimization process. Future steps should include the design of a special training paradigm as well as a pilot study for confirming the theoretical approach proposed in this work

Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition

<p>Abstract</p> <p>Background</p> <p>Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased.</p> <p>Methods</p> <p>This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR).</p> <p>Results</p> <p>The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (<it>P </it>< 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis.</p> <p>Conclusions</p> <p>The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.</p

Effects of Sensorimotor Perturbations on Balance Performance and Electrocortical Dynamics

Humans must frequently adapt their posture to prevent loss of balance. Such balance control requires complex, precisely-timed coordination among sensory input, neural processing, and motor output. Despite its importance, our current understanding of cortical involvement during balance control remains limited by traditional neuroimaging methods, which are stationary and have poor time resolution. High-density electroencephalography (EEG), combined with independent component analysis, has become a promising tool for recording cortical dynamics during balance perturbations due to its portability and high temporal resolution. Additionally, recent improvements in immersive virtual reality headsets may provide new rehabilitative paradigms, but the effects of virtual reality on balance and cortical function remain poorly understood. In my first study, I recorded high-density EEG from healthy, young adult subjects as they walked along a beam with and without virtual reality high heights exposure. While virtual high heights did induce stress, the use of virtual reality during the task increased performance errors and EEG measures of cognitive loading compared to real-world viewing without a headset. In my second study, I collected high-density EEG from healthy young adults as they walked along a treadmill-mounted balance beam to determine the effect of a transient visual perturbation on training in virtual reality. Subjects in the perturbations group improved comparably to those that trained without virtual reality, indicating that the perturbation helped subjects overcome the negative effects of virtual reality on motor learning. The perturbation primarily elicited a cognitive change. In my third study, healthy, young adult EEG was recorded during physical pull and visual rotation perturbations to tandem walking and tandem standing. I found similar electrocortical patterns for both perturbation types, but different cortical areas were involved for each. In my fourth study, I used a phantom head to validate EEG connectivity methods based on Granger causality in a real-world environment. In general, connectivity measures could determine the underlying connections, but many were susceptible to high-frequency false positives. Using data from my third study, my fifth study analyzed corticomuscular connectivity patterns following sensorimotor balance perturbations. I found strong occipito-parietal connections regardless of perturbation type, along with evidence of direct muscular control from the supplementary motor area during the standing perturbation response. Taken together, the work presented in this dissertation greatly expands upon the current knowledge of cortical processing during sensorimotor balance perturbations and the effect of such perturbations on short-term motor learning, providing multiple avenues for future exploration.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147615/1/stepeter_1.pd

Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements

Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8-13 Hz) and beta (13-30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas

Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis

The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. In this paper, we study how physiological artifacts (i.e., eye movements, motion artifacts, muscle artifacts and compensatory movements with the other limb) can affect EEG activity of stroke patients. Data from 31 severely paralyzed stroke patients performing/attempting grasping movements with their healthy/paralyzed hand were analyzed offline. We estimated the cortical activation as the event-related desynchronization (ERD) of sensorimotor rhythms and used it to detect the movements with a pseudo-online simulated BMI. Automated state-of-the-art methods (linear regression to remove ocular contaminations and statistical thresholding to reject the other types of artifacts) were used to minimize the influence of artifacts. The effect of artifact reduction was quantified in terms of ERD and BMI performance. The results reveal a significant contamination affecting the EEG, being involuntary muscle activity the main source of artifacts. Artifact reduction helped extracting the oscillatory signatures of motor tasks, isolating relevant information from noise and revealing a more prominent ERD activity. Lower BMI performances were obtained when artifacts were eliminated from the training datasets. This suggests that artifacts produce an optimistic bias that improves theoretical accuracy but may result in a poor link between task-related oscillatory activity and BMI peripheral feedback. With a clinically relevant dataset of stroke patients, we evidence the need of appropriate methodologies to remove artifacts from EEG datasets to obtain accurate estimations of the motor brain activity.This study was funded by the fortüne-Program of the University of Tübingen (2422-0-1 and 2452-0-0), the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053) and AMORSA (FKZ 16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the Basque Government Science Program (EXOTEK: KK 2016/00083). The work of A. Insausti-Delgado was supported by the Basque Government's scholarship for predoctoral students

Assessing the depth of cognitive processing as the basis for potential user-state adaptation

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli

Cortical Oscillations During a Lateral Balance Perturbation While Walking

The role of sensory systems in the cortical control of dynamic balance was examined using electroencephalography (EEG) recordings during balance perturbations while walking. Specifically, we examined the impact of sensory deficits on cortical oscillations using vibratory stimuli to suppress sensory feedback and by comparing cortical oscillations during balance perturbations while walking in people with sensory deficits associated with cervical myelopathy and neurologically intact controls. Balance during walking provides a rich framework for investigating cortical control using EEG during a functionally relevant task. While this approach is promising, substantial technical challenges remain in recording and processing EEG in the noisy, artifact laden environment associated with walking. We therefore first investigated the role of sensory attenuation in healthy, adult controls within the framework of a simple, motor task. We then examined the effectiveness of using independent component analysis and additional machine learning techniques such as clustering and linear classifiers for differentiating noise from actual brain activity in EEG signals during walking. Finally, we examined a more complicated experimental framework using a custom cable-servomotor system to deliver a lateral pull to the waist of participants with cervical myelopathy while walking and measured their cortical activity using high density EEG. We observed that the attenuation of sensory input in healthy controls induced a similar change in beta band modulation as found previously in spinal cord injury for simple movements of the ankle. During walking, large increases in theta band power throughout the cortex were observed to modulate with lateral balance perturbations. Theta band modulations in the frontal areas of the cortex were significantly delayed in time and displayed a more spatially lateralized cortical localization for participants with cervical myelopathy compared to age-matched, healthy controls. The timing of these theta power modulations were significantly correlated with the initiation of a widening step width correction in response to the balance perturbation. Our results support a link between the modulation of cortical oscillations and sensorimotor integration in simple and complex motor paradigms