Abstract
Recent mobile brain/body imaging (MoBI) techniques based on active electrode scalp electroencephalogram (EEG) allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h) using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject’s head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects’ motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.
Highlights
To assess for the potential effects of head motion-related artifacts on the signal quality of the EEG, we analyzed the spectral content of the EEG and acceleration data to inspect for common harmonics in the spectra and computed the ERSPs to measure the deviations from baseline in spectral power across the average gait cycle
We found that the patterns in these measurements are not similar between individual EEG channels and the acceleration, in that strong harmonics in the Fourier spectra and strong spectral gait-synced spectral deviations were found for acceleration but these harmonics were generally absent or negligible in the scalp EEG at gait speeds no faster than 3.0 km/h with minor evidence of motion artifacts in the 4.5 km/h condition in the case of peripheral electrodes
Comparisons of these findings with results from Artifact Subspace Reconstruction (ASR)-cleaned EEG signals suggest that motion artifacts did not likely affect the EEG signals
Summary
The development of non-invasive mobile brain/body imaging (MoBI) techniques based on active electroencephalography (EEG) synchronized with motion sensing (Makeig et al, 2009; Gramann et al, 2014) and advanced signal processing methods to identify and remove physiological and non-physiological artifacts (Rong and Contreras-Vidal, 2006; Velu and de Sa, 2013; Lau et al, 2014; Urigüen and Garcia-Zapirain, 2015) promise to allow neuroscientists and engineers to investigate the neural dynamics in brain networks during natural (i.e., unconstrained environments) cognition and action.Recent advances in non-invasive EEG to detect brain activation patterns signaling movement intent during locomotive and non-locomotive tasks (Presacco et al, 2011, 2012; Severens et al, 2012; Bulea et al, 2014; Kline et al, 2014) and during assisted walking using in lower extremityNegligible Motion Artifacts in EEG During Walking wearable exoskeletons (Wagner et al, 2012; Do et al, 2013; Kilicarslan et al, 2013; He et al, 2014; Seeber et al, 2014) offer the potential to elucidate the cortical contributions to gait and the harnessing of such gait-related neural activity for brain-machine interfaces (BMI) to wearable robots for assistive and therapeutic applications (Venkatakrishnan et al, 2014).little is known about the motor circuits for walking in humans. Recent advances in non-invasive EEG to detect brain activation patterns signaling movement intent during locomotive and non-locomotive tasks (Presacco et al, 2011, 2012; Severens et al, 2012; Bulea et al, 2014; Kline et al, 2014) and during assisted walking using in lower extremity. Negligible Motion Artifacts in EEG During Walking wearable exoskeletons (Wagner et al, 2012; Do et al, 2013; Kilicarslan et al, 2013; He et al, 2014; Seeber et al, 2014) offer the potential to elucidate the cortical contributions to gait and the harnessing of such gait-related neural activity for brain-machine interfaces (BMI) to wearable robots for assistive and therapeutic applications (Venkatakrishnan et al, 2014). That primary motor cortex carries information about bipedal locomotion has been directly proven by the work of Fitzsimmons et al (2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal walking in rhesus macaques
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