Time-frequency analysis methods and their application in developmental EEG data

Morales, Santiago; Bowers, Maureen E.

doi:10.1016/j.dcn.2022.101067

Cited by 61 publications

(43 citation statements)

References 49 publications

(63 reference statements)

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“…The time-frequency analysis of auto spectra and CMC was computed in a typical participant. Time frequency analysis can show the dynamic changes of neural mechanisms with time and frequency ( Morales and Bowers, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Effects of auditory feedback on fine motor output and corticomuscular coherence during a unilateral finger pinch task

2022

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Auditory feedback is important to reduce movement error and improve motor performance during a precise motor task. Accurate motion guided by auditory feedback may rely on the neural muscle transmission pathway between the sensorimotor area and the effective muscle. However, it remains unclear how neural activities and sensorimotor loops play a role in enhancing performance. The present study uses an auditory feedback system by simultaneously recording electroencephalogram (EEG), electromyography (EMG), and exert force information to measure corticomuscular coherence (CMC), neural activity, and motor performance during precise unilateral right-hand pinch by using the thumb and the index finger with and without auditory feedback. This study confirms three results. First, compared with no auditory feedback, auditory feedback decreases movement errors. Second, compared with no auditory feedback, auditory feedback decreased the power spectrum in the beta band in the bimanual sensorimotor cortex and the alpha band in the ipsilateral sensorimotor cortex. Finally, CMC was computed between effector muscle of right hand and contralateral sensorimotor cortex. Analyses reveals that the CMC of beta band significantly decreases in auditory feedback condition compared with no auditory feedback condition. The results indicate that auditory feedback decreases the power spectral in the alpha and beta bands and decreases corticospinal connection in the beta band during precise hand control. This study provides a new perspective on the effect of auditory feedback on behavior and brain activity and offers a new idea for designing more suitable and effective rehabilitation and training strategies to improve fine motor performance.

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Section: Methodsmentioning

confidence: 99%

Effects of auditory feedback on fine motor output and corticomuscular coherence during a unilateral finger pinch task

2022

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“…In this case, it is possible to first compute a dynamic representation for the power content for each epoch in the collection separately, and then average these representations together to increase the signal-to-noise ratio as before. In Meggie, wavelets are used to extract a time course for each of the frequencies of interest separately, which are then stacked together to create a time-frequency representation (Morales and Bowers, 2022).…”

Section: Induced Responsesmentioning

confidence: 99%

“…activity emerging due to internal computation not necessitating external triggers). It is only in the 2000s when the tools to do more than just ERP analysis have become widely available (Li et al, 2013;Morales and Bowers, 2022) Currently, the state-of-the-art methodology is developing rapidly. In addition to ERPs, resting state analysis based on spectral decompositions and induced response analysis with wavelets are considered standard.…”

Section: Introductionmentioning

confidence: 99%

Meggie – easy-to-use graphical user interface for M/EEG analysis based on MNE-python

Heinilä

Parviainen

2022

Preprint

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In the last decades, electrophysiological imaging methodology has seen many advances and the computational power in the neuroscience laboratories has steadily increased. Still, the new methodologies are unavailable for many. There is a need for more versatile analysis approaches for neuroscience specialists without a programming background. Using a software which provides standard pipelines, provides good default values for parameters, has a good multi-subject support, and stores the used analysis steps with the parameters in one place for reporting, is efficient and fast. In addition to enabling analysis for people without background in programming, it enables analysis for people with background in programming but a limited background in neuroscience. When constructed with care, the GUI may guide the researcher to apply analysis steps in correct order with reasonable default parameters. Two existing software, EEGLAB and Brainstorm, both provide an easy-to-use graphical user interface for end-to-end analysis for multiple subjects. The key difference to work presented here is the choice of language. The scientific community is moving en masse towards the python programming language, thus making it an ideal platform for extendable software. Another problem with Matlab is that it is not free - both from the perspective of open source and concrete monetary resources. Within the current trend towards increasing open science, covering data, analysis and reporting, the need for open source software is imperative. Meggie is an open source software for running MEG and EEG analysis with easy-to-use graphical user interface. It is written in Python 3, runs on Linux, macOS and Windows, and uses the MNE-python library under the hood to do heavy lifting. It is designed to allow end-to-end analysis of MEG and EEG datasets from multiple subjects with common sensor-level analysis steps such as preprocessing, epoching and averaging, spectral analysis and time-frequency analysis. Most of the analysis steps can be run for all the subjects in one go, and combining the results across subjects is made possible with grand averages. We have emphasized the extendibility of Meggie by implementing most of the Meggie itself as plugins, thus ensuring that new plugins have access to all necessary core features. Meggie answers the demand for easy-to-use and extendable python-based graphical user interface that provides an end-to-end analysis environment for M/EEG data analysis. It is freely available at https://github.com/cibr-jyu/meggie under the BSD license. Installation instructions, documentation and tutorials are found on that website.

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“…It is well-known that time–frequency analysis is the cornerstone of seizure detection systems [ 13 , 14 ]. It can capture the transient information of nonstationary signals in the time and frequency domains, paving the way for subsequent feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

et al. 2022

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Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

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Time-frequency analysis methods and their application in developmental EEG data

Cited by 61 publications

References 49 publications

Effects of auditory feedback on fine motor output and corticomuscular coherence during a unilateral finger pinch task

Effects of auditory feedback on fine motor output and corticomuscular coherence during a unilateral finger pinch task

Meggie – easy-to-use graphical user interface for M/EEG analysis based on MNE-python

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

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