Exploratory EEG data analysis for psychophysiological experiments

Marroquín, José L.; Harmony, Thalı́a; Rodríguez, Verónica; Valdés, P.

doi:10.1016/j.neuroimage.2003.10.031

Cited by 16 publications

(20 citation statements)

References 23 publications

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“…The results of this analysis, presented below, coincide to great extent with previous findings [14] [9], supporting our hypothesis that a pattern-oriented segmentation of the TF plane may reveal task-related psychophysiological processes. A detailed, quantitative study of the Words/Figures experiment, including statistical comparisons between both conditions, is, however, beyond the scope of this work.…”

Section: Validation: Psychophysiological Analysis Of the Word And Figsupporting

confidence: 90%

“…The decrease in power during the whole trial (100-1400 ms) is observed in the frequencies from 7 to 40 Hz and is present in both tasks (Region 1 for Figures and 4 for Words) and similar to previous observations performed with a different algorithm [14]. These changes have been related to attention in another mental tasks by different authors, and for this reason we consider that they represent activation of attentional processes.…”

Section: Power Changessupporting

confidence: 89%

“…To obtain a visual representation of the synchrony dynamics across time and frequency, one can reduce one of the two spatial dimensions, and plot the results in a time-frequency-topography (TFT) map [14]. We do this by counting, for each electrode e, the number h + f,e (t) of electrodes whose synchronization with e increases at time t and frequency f ; that is,…”

Section: Visualizationmentioning

confidence: 99%

“…In other words, power is estimated independently for each electrode, and thus the dimensionality of power data is typically lower than the dimensionality of synchrony data. In a previous work [14], Marroquin et al proposed a methodology for the segmentation of the TF plane in terms of patterns of power changes, which were called activation patterns. The procedure described in that work was fully based on a Bayesian approach with an MRF prior model, which was relatively expensive in computational terms.…”

Section: Power Dynamicsmentioning

confidence: 99%

“…This type of representation is used in various works [2] [13], [11]; however, because it focuses on only one pair of electrodes, it fails to show the spatial distribution of the synchronous processes. In previous works [14] [9], we introduced a time-frequency-topography (TFT) visualization system which was able to display significant changes in synchronization across a wide time-frequency region either by reducing one spatial dimension (i.e., results were displayed for each electrode instead of each electrode-pair), or by averaging across relatively large time-frequency windows (and thus reducing the resolution of the analysis). While this methodology produces useful results, it presents a few shortcomings: first, in order to obtain a comprehensive display of synchronization dynamics for all electrode pairs, one has to estimate a statistic of the synchrony changes across an arbitrarily chosen time-frequency window (e.g., obtained from a regular partition of the time-frequency plane), and it is assumed that the synchronization pattern remains relatively constant across the window.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classification and interactive segmentation of EEG synchrony patterns

Alba

Marroquín

Arce-Santana

et al. 2010

Pattern Recognition

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This paper presents a novel methodology for the exploratory analysis of power and synchronization patterns in EEG data from psychophysiological experiments. The methodology is based on the segmentation of the time-frequency plane in regions with relatively homogeneous synchronization patterns, which is performed by means of a seeded region-growing algorithm, and a Bayesian regularization procedure. We have implemented these methods in an interactive application for the study of cognitive experiments, although some of the techniques discussed in this work can also be applied to other multidimensional data sets. To demonstrate our methodology, results corresponding to a figure and word categorization EEG experiment are presented.

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Section: Validation: Psychophysiological Analysis Of the Word And Figsupporting

confidence: 90%

Section: Power Changessupporting

confidence: 89%

Section: Visualizationmentioning

confidence: 99%

Section: Power Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification and interactive segmentation of EEG synchrony patterns

Alba

Marroquín

Arce-Santana

et al. 2010

Pattern Recognition

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Parametric analysis of oscillatory activity as measured with EEG/MEG

Kiebel

Tallon‐Baudry

Friston

2005

Human Brain Mapping

131

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Abstract:We assess the suitability of conventional parametric statistics for analyzing oscillatory activity, as measured with electroencephalography/magnetoencephalography (EEG/MEG). The approach we consider is based on narrow-band power time-frequency decompositions of single-trial data. The ensuing power measures have a 2 -distribution. The use of the general linear model (GLM) under normal error assumptions is, therefore, difficult to motivate for these data. This is unfortunate because the GLM plays a central role in classical inference and is the standard estimation and inference framework for neuroimaging data. The key contribution of this work is to show that, in many circumstances, one can appeal to the central limit theorem and assume normality for generative models of power. If this is not appropriate, one can transform the data to render the error terms approximately normal. These considerations allow one to analyze induced and evoked oscillations using standard frameworks like statistical parametric mapping. We establish the validity of parametric tests using synthetic and real data and compare its performance to established nonparametric procedures. Hum Brain Mapp 26:170 -177, 2005.

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Magnetoencephalographic study of event‐related fields and cortical oscillatory changes during cutaneous warmth processing

Lim

Lee

et al. 2018

Human Brain Mapping

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Thermoreception is an important cutaneous sense, which plays a role in the maintenance of our body temperature and in the detection of potential noxious heat stimulation. In this study, we investigated event‐related fields (ERFs) and neural oscillatory activities, which were modulated by warmth stimulation. We developed a warmth stimulator that could elicit a warmth sensation, without pain or tactile sensation, by using a deep‐penetrating 980‐nm diode laser. The index finger of each participant (n = 24) was irradiated with the laser warmth stimulus, and the cortical responses were measured using magnetoencephalography (MEG). The ERFs and oscillatory responses had late latencies (∼1.3 s and 1.0–1.5 s for ERFs and oscillatory responses, respectively), which could be explained by a slow conduction velocity of warmth‐specific C‐fibers. Cortical sources of warmth‐related ERFs were seen in the bilateral primary and secondary somatosensory cortices (SI and SII), posterior part of the anterior cingulate cortex (pACC), ipsilateral primary motor, and premotor cortex. Thus, we suggested that SI, SII, and pACC play a role in processing the warmth sensation. Time–frequency analysis demonstrated the suppression of the alpha (8–13 Hz) and beta (18–23 Hz) band power in the bilateral sensorimotor cortex. We proposed that the suppressions in alpha and beta band power are involved in the automatic response to the input of warmth stimulation and sensorimotor interactions. The delta band power (1–4 Hz) increased in the frontal, temporal, and cingulate cortices. The power changes in delta band might be related with the attentional processes during the warmth stimulation.

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Exploratory EEG data analysis for psychophysiological experiments

Cited by 16 publications

References 23 publications

Classification and interactive segmentation of EEG synchrony patterns

Classification and interactive segmentation of EEG synchrony patterns

Parametric analysis of oscillatory activity as measured with EEG/MEG

Magnetoencephalographic study of event‐related fields and cortical oscillatory changes during cutaneous warmth processing

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