Electrocortical activity is coupled to gait cycle phase during treadmill walking

Gwin, Joseph T.; Gramann, Klaus; Makeig, Scott; Ferris, Daniel P.

doi:10.1016/j.neuroimage.2010.08.066

Cited by 406 publications

(552 citation statements)

References 86 publications

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“…Neuroimaging using functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), or electroencephalography (EEG) is currently not available or severely limited in walking humans (Fukuyama et al, 1997;Miyai et al, 2001) and humans are the only truly upright walking primates (Eccles, 1989). Only recently Independent Component Analysis and template based artefact removal have been applied to EEG recordings to recover and examine electrocortical activity coupled to the human gait-cycle (Gwin et al, 2011). More work along these lines is mandatory.…”

Section: The Neuroscience Of Gait and Agencymentioning

confidence: 99%

Agency, gait and self-consciousness

Kannape

Blanke

2012

International Journal of Psychophysiology

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Agency is an important aspect of bodily self-consciousness, allowing us to separate own movements from those induced by the environment and to distinguish own movements from those of other agents. Unsurprisingly, theoretical frameworks for agency such as central monitoring are closely tied to computational models of sensorimotor control. Until recently agency research has largely focussed on goal-directed movements of the upper limbs. In particular, the influence of performance-related sensory cues and the relevance of prediction signals for agency judgements have been studied through a variety of spatio-temporal mismatches between movement and the sensory consequences of movement. However, agents often perform a different type of movement; highly automated movements that involve the entire body such as walking, cycling, and swimming with potentially different agency mechanisms. Here, we review recent work about agency for full-body movements such as gait, highlighting the effects of performance-related visual and auditory cues on gait agency. Gait movements differ from upper limb actions. Gait is cyclic, more rarely immediately goal-directed, and is generally considered one of the most automatic and unconscious actions. We discuss such movement differences with respect to the functional mechanisms of full-body agency and body-part agency by linking these gait agency paradigms to computational models of motor control. This is followed by a selective review of gait control, locomotion, and models of motor control relying on prediction signals and underlining their relevance for full-body agency.

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Section: The Neuroscience Of Gait and Agencymentioning

confidence: 99%

Agency, gait and self-consciousness

Kannape

Blanke

2012

International Journal of Psychophysiology

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“…In comparison, functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) are less restrictive and allow measurements to be made in an environment that can simulate normal and actual daily life. Thus, studies using these methods are capable of measuring brain activity even while the subject is standing or walking [11][12][13]. Neurofeedback training using fNIRS and EEG are currently under development [14,15].…”

Section: Introductionmentioning

confidence: 99%

Brain Activity during the Observation, Imagery, and Execution of Tool Use: An fNIRS/EEG Study

Nakano¹

2012

J Nov Physiother

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“…mental processes and body interactions could be monitored and evaluated. There exists a connection between brain activity and locomotion [2], [3]. Another aspect is the usage of biofeedback in training [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…These electrodes measure the electrical manifestation of the electrical activity of the brain [6]. EEG is the only non-invasive method that allows brain activity to be recorded during movement, as its sensors are lightweight enough and easy to carry [2], [3]. Further, the temporal resolution of EEG is sufficiently high to record brain activity during movement [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, EEG is susceptible to various artifacts like eye movements or eye blinks, power line interference, high-frequency noise, and muscle artifacts [2], [4], [7]- [9]. Various solutions exist for the removal of eye artifacts, like the regression model in the time domain proposed by Gratton and coworkers [10] or in the frequency domain proposed by Woestenburg et al [11].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data

Leutheuser

Gabsteiger

Hebenstreit

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes.Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data.We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.

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Electrocortical activity is coupled to gait cycle phase during treadmill walking

Cited by 406 publications

References 86 publications

Agency, gait and self-consciousness

Agency, gait and self-consciousness

Brain Activity during the Observation, Imagery, and Execution of Tool Use: An fNIRS/EEG Study

Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data

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