Time-Frequency Analysis of Mu Rhythm Activity during Picture and Video Action Naming Tasks

Cuellar, Megan; Toro, Christina M. del

doi:10.3390/brainsci7090114

Cited by 10 publications

(4 citation statements)

References 75 publications

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“…Similar to the alpha band, in the beta band, a significant power suppression was found starting a few 100 ms before the decision report in FAs (and hits), however, the comparison of FAs vs. BPs did not reveal any significant differences between these two conditions in this frequency band. These results suggest that the beta suppression in FAs, is mainly related to motor related beta-band ERD which can be observed during hand and finger movements ( Pfurtscheller and Lopes da Silva, 1999 ; Hari, 2006 ; Cuellar and Del Toro, 2017 ).…”

Section: Discussionmentioning

confidence: 70%

Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

et al. 2021

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Hallucinations and illusions are two instances of perceptual experiences illustrating how perception might diverge from external sensory stimulations and be generated or altered based on internal brain states. The occurrence of these phenomena is not constrained to patient populations. Similar experiences can be elicited in healthy subjects by means of suitable experimental procedures. Studying the neural mechanisms underlying these experiences not only has the potential to expand our understanding of the brain’s perceptual machinery but also of how it might get impaired. In the current study, we employed an auditory signal detection task to induce auditory illusions by presenting speech snippets at near detection threshold intensity embedded in noise. We investigated the neural correlates of auditory false perceptions by examining the EEG activity preceding the responses in speech absent (false alarm, FA) trials and comparing them to speech present (hit) trials. The results of the comparison of event-related potentials (ERPs) in the activation period vs. baseline revealed the presence of an early negativity (EN) and a late positivity (LP) similar in both hits and FAs, which were absent in misses, correct rejections (CR) and control button presses (BPs). We postulate that the EN and the LP might represent the auditory awareness negativity (AAN) and centro-parietal positivity (CPP) or P300, respectively. The event-related spectral perturbations (ERSPs) exhibited a common power enhancement in low frequencies (<4 Hz) in hits and FAs. The low-frequency power enhancement has been frequently shown to be accompanied with P300 as well as separately being a marker of perceptual awareness, referred to as slow cortical potentials (SCP). Furthermore, the comparison of hits vs. FAs showed a significantly higher LP amplitude and low frequency power in hits compared to FAs. Generally, the observed patterns in the present results resembled some of the major neural correlates associated with perceptual awareness in previous studies. Our findings provide evidence that the neural correlates associated with conscious perception, can be elicited in similar ways in both presence and absence of externally presented sensory stimuli. The present findings did not reveal any pre-stimulus alpha and beta modulations distinguishing conscious vs. unconscious perceptions.

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Section: Discussionmentioning

confidence: 70%

Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

et al. 2021

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“…Univariate time-frequency analysis was conducted at the channel level by computing the power of event-related desynchronization/synchronization (ERD/ERS) using eventrelated spectral perturbations (ERSP)'s. The ERSP is a known measure of average dynamic changes in amplitude of the specified broad band EEG frequency spectrum, as a function of time relative to an experimental event (Cuellar and Toro, 2017;Makeig, 1993;Pfurtscheller and Lopes, 1999). To obtain the time-frequency data, a short-time Fourier transform was computed across the averaged baseline-corrected trials by extracting 200 time points in steps of 10ms, using a Hanning-tapered sliding time window with a fixed length of 500ms, covering the entire epoch from -1000ms to 1500ms.…”

Section: Univariate Cluster-based Analysismentioning

confidence: 99%

“…apples and wine glasses), and tested on decoding performance against the average of 72 trials (36 for each visual object category) in each condition. This was carried out in both the alpha (8-13 Hz) and beta (15-25 Hz) frequency bands (see Coll et al, 2017;Cuellar & Toro, 2017), in both the central and occipital ROI's, to test whether discriminable patterns of information could be detected for different familiar or unfamiliar visual objects in each ROI and frequency band. This analysis was performed on 20 randomly partitioned training test set iterations across the entire 1000ms stimulus duration time window.…”

Section: Multivariate Pattern Analysismentioning

confidence: 99%

Decoding familiar visual object categories in the mu rhythm oscillatory response

Bailey

Sami

Smith

2023

Preprint

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Whilst previous research has linked attenuation of the mu rhythm to the observation of specific visual categories, and even to a potential role in action observation via a putative mirror neuron system, much of this work has not considered what specific type of information might be coded in this oscillatory response when triggered via vision. Here, we sought to determine whether the mu rhythm contains content-specific information about the identity of familiar (and also unfamiliar) graspable objects. In the present study, right-handed participants (N=27) viewed images of both familiar (apple, wine glass) and unfamiliar (cubie, smoothie) graspable objects, whilst performing an orthogonal task at fixation. Multivariate pattern analysis (MVPA) revealed significant decoding of familiar, but not unfamiliar, visual object categories in the mu rhythm response. Thus, simply viewing familiar graspable objects may automatically trigger activation of associated tactile and/or motor properties in sensorimotor areas, reflected in the mu rhythm. In addition, we report significant attenuation in the central beta band for both familiar and unfamiliar visual objects, but not in the mu rhythm. Our findings highlight how analysing two different aspects of the oscillatory response, either attenuation or the representation of information content, provide complementary views on the role of the mu rhythm in response to viewing graspable object categories.

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“…Importantly, mu rhythm does not react to opening or closing the eyes ( Kuhlman, 1978 ). Despite the described differences, the automatic separation of mu from alpha waves in EEG is challenging and drawing the attention of many methodological studies ( Cuellar and del Toro, 2017 ; Garakh et al, 2020 ). Still, the identification of mu rhythm often requires visual inspection and expertise.…”

Section: Introductionmentioning

confidence: 99%

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Soghoyan

Nekrashevich

Ledovsky

et al. 2021

Front. Neuroinform.

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Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

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Time-Frequency Analysis of Mu Rhythm Activity during Picture and Video Action Naming Tasks

Cited by 10 publications

References 75 publications

Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

Similar EEG Activity Patterns During Experimentally-Induced Auditory Illusions and Veridical Perceptions

Decoding familiar visual object categories in the mu rhythm oscillatory response

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

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