MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

doi:10.3389/fnins.2017.00601

Cited by 149 publications

(115 citation statements)

References 47 publications

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“…Unfortunately all such references are doomed to fail since there is no point on the scalp or body surface where the potential is actually zero or a constant [ 29 ].” An alternative approach is the reference electrode standardization technique (REST). REST standardizes a reference point at infinity in recovering temporal information in EEG [ 30 , 31 ]. As more and more studies show the choice of reference points impacts EEG spectral and connectivity analysis [ 32 – 35 ], the generalizability of our results is limited.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalography power and coherence changes with age and motor skill development across the first half year of life

et al. 2018

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Existing research in infants has correlated electroencephalography (EEG) measures of power and coherence to cognitive development and to locomotor experience, but only in infants older than 5 months of age. Our goal was to explore the relationship between EEG measures of power and coherence and motor skill development in younger infants who are developing reaching skill. Twenty-one infants with typical development between 38 and 203 days of age participated. Longitudinal EEG recording sessions were recorded in monthly increments, with 3–5 sessions acquired for 19 participants and 1 session for 2 participants, resulting in 71 sessions in total. EEG variables of interest were relative power in the 6–9 Hz range and coherence between selected electrode pairs. We describe the development of the peak in relative power in the 6–9 Hz frequency band of EEG; it is not present around 1 month of age and starts to appear across the following months. Coherence generally increased in the bilateral frontal-parietal networks, while the interhemispheric connectivity in motor cortices generally decreased. The results of this relatively small pilot study provide a foundational description of neural function changes observed as motor skills are changing across the first half year of life. This is a first step in understanding experience-dependent plasticity of the infant brain and has the potential to aid in the early detection of atypical brain development.

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Section: Discussionmentioning

confidence: 99%

Electroencephalography power and coherence changes with age and motor skill development across the first half year of life

et al. 2018

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“…The EEG was digitized at 500 Hz using BrainAmp amplifiers and BrainVision recorder software (BrainProducts GmbH, Munich, Germany) with an initial reference at FCz. The data was then re-referenced offline using the Reference Electrode Standardization Technique to standardize the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference (Yao, 2001 ; Dong et al, 2017 ). To avoid corrupting the raw data, we manually extracted segments of artifact-free EEG data by visually identifying and then removing vertical eye movements, blinks, muscle activity, and other artifact sources.…”

Section: Methodsmentioning

confidence: 99%

Measuring Brain Complexity During Neural Motor Resonance

2018

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Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8–13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics.Methods: EEG was recorded from healthy participants (n = 45) during rest and during an action observation task. Baseline brain activity was measured followed by participants observing videos of hands squeezing stress balls. We used multiscale entropy (MSE) to quantify the complexity of the mu rhythm during MR. We then performed post-hoc graph theory analysis to explore whether nonlinear dynamics during MR affect brain network topology.Results: We found significant mu-desynchronization during the action observation task and that mu entropy was significantly increased during the task compared to rest, while gamma, beta, theta, and delta bands showed decreased entropy. Moreover, resting-state entropy was significantly predictive of the degree of mu desynchronization. We also observed a decrease in the clustering coefficient in the mu band only and a significant decrease in global alpha efficiency during action observation. MSE during action observation was strongly correlated with alpha network efficiency.Conclusions: The current findings suggest that the desynchronization of the mu wave during MR results in a local increase of mu entropy in sensorimotor areas, potentially reflecting a release from alpha inhibition. This release from inhibition may be mediated by the baseline MSE in the mu band. The dynamical complexity and network analysis of EEG may provide a useful addition for future studies of MR by incorporating measures of nonlinearity.

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“…Then, we determined an epoch of the EEG data analysis corresponding to each trial, containing EEG signals 1 s before the onset of the target phase to 3 s after the onset of the item phase; a single epoch spanned 7 s, consisting of 1 s of fixation, 3 s of the target phase and 3 s of the item phase. Also, we eliminated the noisy channels (FP1, FP2) and applied the standardized REST methods to avoid a potential issue of lateralized referencing (Yao, 2001 ; Yao et al, 2005 ; Dong et al, 2017 ). Only 96.06% trials were used after outlier elimination (<120 μV).…”

Section: Methodsmentioning

confidence: 99%

EEG Beta Oscillations in the Temporoparietal Area Related to the Accuracy in Estimating Others' Preference

Park

Kim

Sohn

et al. 2018

Front. Hum. Neurosci.

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Humans often attempt to predict what others prefer based on a narrow slice of experience, called thin-slicing. According to the theoretical bases for how humans can predict the preference of others, one tends to estimate the other's preference using a perceived difference between the other and self. Previous neuroimaging studies have revealed that the network of dorsal medial prefrontal cortex (dmPFC) and right temporoparietal junction (rTPJ) is related to the ability of predicting others' preference. However, it still remains unknown about the temporal patterns of neural activities for others' preference prediction through thin-slicing. To investigate such temporal aspects of neural activities, we investigated human electroencephalography (EEG) recorded during the task of predicting the preference of others while only a facial picture of others was provided. Twenty participants (all female, average age: 21.86) participated in the study. In each trial of the task, participants were shown a picture of either a target person or self for 3 s, followed by the presentation of a movie poster over which participants predicted the target person's preference as liking or disliking. The time-frequency EEG analysis was employed to analyze temporal changes in the amplitudes of brain oscillations. Participants could predict others' preference for movies with accuracy of 56.89 ± 3.16% and 10 out of 20 participants exhibited prediction accuracy higher than a chance level (95% interval). There was a significant difference in the power of the parietal alpha (10~13 Hz) oscillation 0.6~0.8 s after the onset of poster presentation between the cases when participants predicted others' preference and when they reported self-preference (p < 0.05). The power of brain oscillations at any frequency band and time period during the trial did not show a significant correlation with individual prediction accuracy. However, when we measured differences of the power between the trials of predicting other's preference and reporting self-preference, the right temporal beta oscillations 1.6~1.8 s after the onset of facial picture presentation exhibited a significant correlation with individual accuracy. Our results suggest that right temporoparietal beta oscillations may be correlated with one's ability to predict what others prefer with minimal information.

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MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

Cited by 149 publications

References 47 publications

Electroencephalography power and coherence changes with age and motor skill development across the first half year of life

Electroencephalography power and coherence changes with age and motor skill development across the first half year of life

Measuring Brain Complexity During Neural Motor Resonance

EEG Beta Oscillations in the Temporoparietal Area Related to the Accuracy in Estimating Others' Preference

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