Group-Level Multivariate Analysis in EasyEEG Toolbox: Examining the Temporal Dynamics Using Topographic Responses

Yang, Jinbiao; Zhu, Hao; Tian, Xing

doi:10.3389/fnins.2018.00468

Cited by 13 publications

(20 citation statements)

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“…We have implemented the methods in the TTT package, an open-source toolbox in Python. The interface of importing and exporting ERP data with other Python-based packages are provided, including MNE-Python (Gramfort et al 2013;Gramfort et al 2014) and EasyEEG (Yang, Zhu, & Tian, 2018). Sample code snippets for illustrating the major part of the recommended workflow is included in the supplementary materials.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, we developed an alternative ERP analysis method based on the topography (Tian & Huber, 2008;Tian, Poeppel, & Huber, 2011;Yang, Zhu, & Tian, 2018). A topography of ERP responses is the electric field distribution in all sensors over the scalp.…”

Section: Introductionmentioning

confidence: 99%

“…It represents the configuration of underlying neural processes. We have developed multivariate analysis methods for testing psychological and neuroscience hypotheses by assessing the patterns of topographies between experimental conditions (Tian & Huber, 2008;Tian, Poeppel, & Huber, 2011) and implemented these methods in an open-source toolbox, EasyEEG (Yang, Zhu, & Tian, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…These methods have been implemented in an open-source toolbox, the Topographybased Temporal-analysis Toolbox (TTT, https://github.com/TTT-EEG/TTT-EEG). It also offers an interface to effectively link with our ERP data analysis software EasyEEG (Yang, Zhu, & Tian, 2018, https://github.com/ray306/EasyEEG) and python-based MNE software (Gramfort et al 2013, https://github.com/mne-tools/mne-python).…”

Section: Introductionmentioning

confidence: 99%

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Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Wang¹,

Zhu²,

Tian³

2019

Preprint

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The fine temporal resolution of electroencephalography (EEG) makes it one of the most widely used non-invasive electrophysiological recording methods in cognitive neuroscience research.One of the common ways to explore the neural dynamics is to create event-related potentials (ERPs) by averaging trials, followed by the examination of the response magnitude at peak latencies. However, a complete profile of neural dynamics, including temporal indices of onset time, offset time, duration, and processing speed, is needed to investigate cognitive neural mechanisms. Based on the multivariate topographic analysis, we developed an analytical framework that included two methods to explore neural dynamics in ERPs. The first method separates continuous ERP waveforms into distinct components based on their topographic patterns. Crucial temporal indices such as the peak latency, onset and offset times can be automatically identified and indices about processing speed such as duration, rise, and fall speed can be derived. The second method scrutinizes the temporal dynamics of identified components by reducing the temporal variance among trials. The response peaks of signal trials are identified based on a target topographic template, and temporal-variance-free ERPs are obtained after aligning individual trials. This method quantifies the temporal variance as a new measure of cognitive noise, as well as increases both the accuracy of temporal dynamics estimation and the signal-to-noise ratio (SNR) of the ERP responses. The validity and reliability of these methods were tested with simulation as well as empirical datasets from an attention study and a semantic priming (N400) study. Together, we offer an analytical framework in a data-driven, bias-free manner to investigate neural dynamics in non-invasive scalp recordings. These methods are implemented in the Python-based open-source package TTT (Topography-based Temporalanalysis Toolbox).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Wang¹,

Zhu²,

Tian³

2019

Preprint

Self Cite

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show abstract

“…Our analysis focused on topographic patterns across all sensors rather than the response amplitude in selected groups of sensors, as it can provide more holistic and unbiased information. We used multivariate instead of univariate methods to test our hypotheses because multivariate methods can collectively reflect spatial and temporal information and offer more power to test psychological and neuroscience hypothesis by overcoming problems such as individual differences, sensor selection and reference selection in EEG (Murray, Brunet, & Michel, 2008; Tian & Huber, 2008; Tian, Poeppel, & Huber, 2011; Yang, Zhu, & Tian, 2018). Three multivariate based methods as follows were applied.…”

Section: Methodsmentioning

confidence: 99%

How do we segment text? Two-stage chunking operation in reading

Yang

Cai

Tian

2019

Preprint

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AbstractChunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in reader’s mental lexicon. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the mechanisms of chunking remain elusive, especially in reading given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking operation that mediates the reading of the text that normally contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels of grain-size in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Participants were asked to make lexical decision on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) were recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of four-character global chunk, but not vice versa, which indicated that the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order -- the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent behavioral and EEG results suggest that chunking in reading occurs at multiple levels via a two-stage operation -- simultaneous detection and global-first recognition.Significance StatementThe learners of a new language often read word by word. But why can proficient readers read multiple words at a time? The current study investigates how we efficiently segment a complicate text into smaller pieces and how we process these pieces. Participants read Chinese strings with different structures while their key-press responses and brain EEG signals were recorded. We found that texts were quickly (about 100 ms from their occurrences) segmented to varied sizes of pieces, and larger pieces were then processed earlier than small pieces. Our results suggest that readers can use existing knowledge to efficiently segment and process written information.

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EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

Zerna

Strobel

Scheffel

2021

Sci Rep

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In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

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Group-Level Multivariate Analysis in EasyEEG Toolbox: Examining the Temporal Dynamics Using Topographic Responses

Cited by 13 publications

References 20 publications

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

How do we segment text? Two-stage chunking operation in reading

EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

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