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

Lau, Troy; Gwin, Joseph T.; McDowell, Kaleb; Ferris, Daniel P.

doi:10.1186/1743-0003-9-47

Cited by 76 publications

(60 citation statements)

References 32 publications

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“…The authors recommended using epoch rejection rate, pre-stimulus noise, signal-to-noise ratio, and amplitude variance across the P300 event window to evaluate EEG hardware systems and artifact identification and removal efficacy in walking studies. In a different approach, Lau et al (2012) used a weighted phase lag index across all channels and recovered a P300 response to a target stimulus while subjects stood or walked on a treadmill.…”

Section: Resultsmentioning

confidence: 99%

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Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

114

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This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

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Section: Resultsmentioning

confidence: 99%

“…Interestingly, only two studies investigated brain activity in both static and dynamic balance control tasks (Lau et al, 2012; Mirelman et al, 2014). Neither of these studies used a mechanical perturbation to challenge balance.…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

114

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“…Our laboratory has recorded electrocortical spectral fluctuations during treadmill walking in healthy, young adults (Gwin et al, 2011; Sipp et al, 2013). Other groups have also proved the feasibility of measuring scalp electrocortical signals during human walking, at speeds ranging from 0.42 m/s to 1.9 m/s, to provide insight into brain function (Gramann et al, 2010; Cheron et al, 2012; Lau et al, 2012; Severens et al, 2012; Wagner et al, 2012; Seeber et al, 2014; Seeber et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

Isolating gait-related movement artifacts in electroencephalography during human walking

et al. 2015

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Objective High-density electroencephelography (EEG) can provide 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 0.4–1.6 m/s. 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 removing of EEG movement artifact to advance the field.

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“…II) The study of new electrophysiological markers that may be used to improve a BCI decoder in addition to the traditional (steady-state) event-related potentials and event-related (de)synchronization, such as global synchronization [17] and phase-lag index variance [18].…”

Section: I)mentioning

confidence: 99%