Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

Download Full-text

Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults

10.1101/268995 ◽

2018 ◽

Author(s):

Luis F. Ciria ◽

Pandelis Perakakis ◽

Antonio Luque-Casado ◽

Daniel Sanabria

Keyword(s):

Physical Exercise ◽

Inhibitory Control ◽

Frequency Band ◽

Brain Function ◽

Acute Exercise ◽

Brain Activity ◽

Oscillatory Activity ◽

Power Increase ◽

Oscillatory Brain Activity ◽

Exercise Induced

AbstractExtant evidence suggests that acute exercise triggers a tonic power increase in the alpha frequency band at frontal locations, which has been linked to benefits in cognitive function. However, recent literature has questioned such a selective effect on a particular frequency band, indicating a rather overall power increase across the entire frequency spectrum. Moreover, the nature of task-evoked oscillatory brain activity associated to inhibitory control after exercising, and the duration of the exercise effect, are not yet clear. Here, we investigate for the first time steady state oscillatory brain activity during and following an acute bout of aerobic exercise at two different exercise intensities (moderate-to-high and light), by means of a data-driven cluster-based approach to describe the spatio-temporal distribution of exercise-induced effects on brain function without prior assumptions on any frequency range or site of interest. We also assess the transient oscillatory brain activity elicited by stimulus presentation, as well as behavioural performance, in two inhibitory control (flanker) tasks, one performed after a short delay following the physical exercise and another completed after a rest period of 15’ post-exercise to explore the time course of exercise-induced changes on brain function and cognitive performance. The results show that oscillatory brain activity increases during exercise compared to the resting state, and that this increase is higher during the moderate-to-high intensity exercise with respect to the light intensity exercise. In addition, our results show that the global pattern of increased oscillatory brain activity is not specific to any concrete surface localization in slow frequencies, while in faster frequencies this effect is located in parieto-occipital sites. Notably, the exercise-induced increase in oscillatory brain activity disappears immediately after the end of the exercise bout. Neither transient (event-related) oscillatory activity, nor behavioral performance during the flanker tasks following exercise showed significant between-intensity differences. The present findings help elucidate the effect of physical exercise on oscillatory brain activity and challenge previous research suggesting improved inhibitory control following moderate-to-high acute exercise.

Download Full-text

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500580 ◽

2013 ◽

Vol 25 (06) ◽

pp. 1350058 ◽

Cited By ~ 2

Author(s):

Pablo F. Diez ◽

Vicente A. Mut ◽

Eric Laciar ◽

Abel Torres ◽

Enrique M. Avila Perona

Keyword(s):

Empirical Mode Decomposition ◽

Communication System ◽

Brain Activity ◽

Features Extraction ◽

Eeg Signals ◽

Linear Discriminant ◽

Mode Decomposition ◽

Machine Communication ◽

Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

Download Full-text

Auditory modulation of oscillatory activity in extra-striate visual cortex and its contribution to audio–visual multisensory integration: A human intracranial EEG study

Seeing and Perceiving ◽

10.1163/187847612x648279 ◽

2012 ◽

Vol 25 (0) ◽

pp. 198

Author(s):

Manuel R. Mercier ◽

John J. Foxe ◽

Ian C. Fiebelkorn ◽

John S. Butler ◽

Theodore H. Schwartz ◽

...

Keyword(s):

Visual Cortex ◽

Multisensory Integration ◽

Multisensory Processing ◽

Brain Activity ◽

Visual Stimuli ◽

Additive Model ◽

Oscillatory Activity ◽

Time Frequency ◽

Electrical Brain Activity ◽

Sensory Cortices

Investigations have traditionally focused on activity in the sensory cortices as a function of their respective sensory inputs. However, converging evidence from multisensory research has shown that neural activity in a given sensory region can be modulated by stimulation of other so-called ancillary sensory systems. Both electrophysiology and functional imaging support the occurrence of multisensory processing in human sensory cortex based on the latency of multisensory effects and their precise anatomical localization. Still, due to inherent methodological limitations, direct evidence of the precise mechanisms by which multisensory integration occurs within human sensory cortices is lacking. Using intracranial recordings in epileptic patients () undergoing presurgical evaluation, we investigated the neurophysiological basis of multisensory integration in visual cortex. Subdural electrical brain activity was recorded while patients performed a simple detection task of randomly ordered Auditory alone (A), Visual alone (V) and Audio–Visual stimuli (AV). We then performed time-frequency analysis: first we investigated each condition separately to evaluate responses compared to baseline, then we indexed multisensory integration using both the maximum criterion model (AV vs. V) and the additive model (AV vs. A+V). Our results show that auditory input significantly modulates neuronal activity in visual cortex by resetting the phase of ongoing oscillatory activity. This in turn leads to multisensory integration when auditory and visual stimuli are simultaneously presented.

Download Full-text

The separation of auditory experience and the temporal structure of MEG recorded brain activity

10.1101/189480 ◽

2017 ◽

Author(s):

Chris Allen

Keyword(s):

High Frequency ◽

Temporal Dynamics ◽

Brain Activity ◽

Temporal Structure ◽

Oscillatory Activity ◽

Auditory Stimuli ◽

Brain Oscillations ◽

Successful Separation ◽

Auditory Experience ◽

Oscillatory Brain Activity

AbstractDo brain oscillations limit the temporal dynamics of experience? This pre-registered study used the separation of auditory stimuli to track perceptual experience and related this to oscillatory activity using magnetoencephalography. The rates at which auditory stimuli could be individuated matched the rates of oscillatory brain activity. Stimuli also entrained brain activity at the frequencies at which they were presented and a progression of high frequency gamma band events appeared to predict successful separation. These findings support a generalised function for brain oscillations, across frequency bands, in the alignment of activity to delineate representations.

Download Full-text

Oscillations in the central brain ofDrosophilaare phase locked to attended visual features

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2010749117 ◽

2020 ◽

Vol 117 (47) ◽

pp. 29925-29936

Author(s):

Martyna J. Grabowska ◽

Rhiannon Jeans ◽

James Steeves ◽

Bruno van Swinderen

Keyword(s):

Brain Activity ◽

Visual Navigation ◽

Central Complex ◽

Oscillatory Activity ◽

Visual Features ◽

Attention Tasks ◽

Temporal Features ◽

Object Based ◽

Oscillatory Brain Activity ◽

Beta Frequency

Object-based attention describes the brain’s capacity to prioritize one set of stimuli while ignoring others. Human research suggests that the binding of diverse stimuli into one attended percept requires phase-locked oscillatory activity in the brain. Even insects display oscillatory brain activity during visual attention tasks, but it is unclear if neural oscillations in insects are selectively correlated to different features of attended objects. We addressed this question by recording local field potentials in theDrosophilacentral complex, a brain structure involved in visual navigation and decision making. We found that attention selectively increased the neural gain of visual features associated with attended objects and that attention could be redirected to unattended objects by activation of a reward circuit. Attention was associated with increased beta (20- to 30-Hz) oscillations that selectively locked onto temporal features of the attended visual objects. Our results suggest a conserved function for the beta frequency range in regulating selective attention to salient visual features.

Download Full-text

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas

Journal of Neurophysiology ◽

10.1152/jn.00543.2011 ◽

2012 ◽

Vol 107 (9) ◽

pp. 2475-2484 ◽

Cited By ~ 20

Author(s):

Paolo Manganotti ◽

Emanuela Formaggio ◽

Silvia Francesca Storti ◽

Daniele De Massari ◽

Alessandro Zamboni ◽

...

Keyword(s):

Frequency Analysis ◽

Primary Motor Cortex ◽

Brain Activity ◽

High Rate ◽

Experimental Session ◽

Perturbation Approach ◽

Eeg Rhythms ◽

Time Frequency Analysis ◽

Time Frequency ◽

Oscillatory Brain Activity

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1). A time-frequency analysis based on the wavelet method was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS. Single, paired, and transcallosal TMS applied on the sensorimotor areas induced rapid desynchronization over the frontal and central-parietal electrodes mainly in the alpha and beta bands, followed by a rebound of synchronization, and rapid synchronization of delta and theta activity. Wavelet analysis after a perturbation approach is a novel way to investigate modulation of oscillatory brain activity. The main findings are consistent with the concept that the human motor system may be based on networklike oscillatory cortical activity and might be modulated by single, paired, and transcallosal magnetic pulses applied to M1, suggesting a phenomenon of fast brain activity resetting and triggering of slow activity.

Download Full-text

EEG correlates of working memory performance in females

10.1101/098301 ◽

2017 ◽

Author(s):

Yuri G. Pavlov ◽

Boris Kotchoubey

Keyword(s):

Working Memory ◽

Individual Differences ◽

High Performance ◽

Brain Activity ◽

Memory Performance ◽

Two Dimensions ◽

Oscillatory Activity ◽

Theta Power ◽

Low Performance ◽

Oscillatory Brain Activity

AbstractBackgroundThe study investigates oscillatory brain activity during working memory (WM) tasks. The tasks employed varied in two dimensions. First, they differed in complexity from average to highly demanding. Second, we used two types of tasks, which required either only retention of stimulus set or retention and manipulation of the content. We expected to reveal EEG correlates of temporary storage and central executive components of WM and to assess their contribution to individual differences.ResultsGenerally, as compared with the retention condition, manipulation of stimuli in WM was associated with distributed suppression of alpha1 activity and with the increase of the midline theta activity. Load and task dependent decrement of beta1 power was found during task performance. Beta2 power increased with the increasing WM load and did not significantly depend on the type of the task.At the level of individual differences, we found that the high performance (HP) group was characterized by higher alpha rhythm power. The HP group demonstrated task-related increment of theta power in the left anterior area and a gradual increase of theta power at midline area. In contrast, the low performance (LP) group exhibited a drop of theta power in the most challenging condition. HP group was also characterized by stronger desynchronization of beta1 rhythm over the left posterior area in the manipulation condition. In this condition, beta2 power increased in the HP group over anterior areas, but in the LP group over posterior areas.ConclusionsWM performance is accompanied by changes in EEG in a broad frequency range from theta to higher beta bands. The most pronounced differences in oscillatory activity between individuals with high and low WM performance can be observed in the most challenging WM task.

Download Full-text

Neural oscillations while remembering traumatic memories in PTSD

10.1101/639310 ◽

2019 ◽

Author(s):

Inbal Reuveni ◽

Noa Herz ◽

Omer Bonne ◽

Tuvia Peri ◽

Shaul Schreiber ◽

...

Keyword(s):

Traumatic Event ◽

Brain Activity ◽

Traumatic Experience ◽

Oscillatory Activity ◽

Traumatic Memory ◽

Trauma Narratives ◽

Frequency Bands ◽

Traumatic Memories ◽

Localization Analysis ◽

Oscillatory Brain Activity

AbstractBackgroundIn posttraumatic stress disorder (PTSD), the traumatic event is often re-experienced through vivid sensory fragments of the traumatic experience. Though the sensory phenomenology of traumatic memories is well established, neural indications for this qualitative experience are lacking. The current study aimed at monitoring the oscillatory brain activity of PTSD patients during directed and imaginal exposure to the traumatic memory using magnetoencephalography (MEG), in a paradigm resembling exposure therapy.MethodsBrain activity of healthy trauma-exposed controls and PTSD participants was measured with MEG as they listened to individualized trauma narratives as well as to a neutral narrative and as they imagined the narrative in detail. Source localization analysis on varied frequency bands was conducted in order to map neural generators of altered oscillatory activity.ResultsPTSD patients exhibited increased power of high-frequency bands over visual areas and increased delta and theta power over auditory areas in response to trauma recollection compared to neutral recollection, while controls did not show such differential activation. PTSD participants also showed abnormal modulation of lower frequencies in the medial prefrontal cortex.ConclusionsElicitation of traumatic memories results in a distinct neural pattern in PTSD patients compared to healthy trauma-exposed individuals. Investigating the oscillatory neural dynamics of PTSD patients can help us better understand the processes underlying trauma re-experiencing.

Download Full-text

Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal

Volume 4B: Dynamics, Vibration, and Control ◽

10.1115/imece2017-71647 ◽

2017 ◽

Cited By ~ 2

Author(s):

MD Erfanul Alam ◽

Biswanath Samanta

Keyword(s):

Empirical Mode Decomposition ◽

Motor Imagery ◽

Signal To Noise Ratio ◽

Brain Activity ◽

Ensemble Empirical Mode Decomposition ◽

Processing Technique ◽

Fluorescent Light ◽

Eeg Signals ◽

Signal Processing Technique ◽

Mode Decomposition

Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterization and identification of motor imagery (MI) activities. EMD has been used for removal of artifacts like electrooculography (EOG) that strongly appears in frontal electrodes of EEG and the power line noise that is mainly produced by the fluorescent light. The performance of EMD has been compared with two extensions, ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD)using signal to noise ratio (SNR). The maximum SNR values found for EMD, EEMD and MEMD are 4.30, 7.64 and 10.62 respectively for the EEG signals considered.

Download Full-text

Instantaneous EEG Signal Analysis Based on Empirical Mode Decomposition Applied to Burst-Suppression in Propofol Anaesthesia

General Reanimatology ◽

10.15360/1813-9779-2021-5-65-79 ◽

2021 ◽

Vol 17 (5) ◽

pp. 65-79

Author(s):

G. Sobolova ◽

M. S. Fabus ◽

M. Fischer ◽

M. Drobny ◽

B. Drobna-Saniova

Keyword(s):

Spectral Analysis ◽

General Anaesthesia ◽

Empirical Mode Decomposition ◽

Brain Activity ◽

Low Frequency ◽

Burst Suppression ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Cyclic Changes ◽

The Brain

The human electroencephalogram (EEG) constitutes a nonstationary, nonlinear electrophysiological signal resulting from synchronous firing of neurons in thalamocortical structures of the brain. Due to the complexity of the brain's physiological structures and its rhythmic oscillations, analysis of EEG often utilises spectral analysis methods.Aim: to improve clinical monitoring of neurophysiological signals and to further explain basic principles of functional mechanisms in the brain during anaesthesia.Material and methods. In this paper we used Empirical Mode decomposition (EMD), a novel spectral analysis method especially suited for nonstationary and nonlinear signals. EMD and the related Hilbert-Huang Transform (HHT) decompose signal into constituent Intrinsic Mode Functions (IMFs). In this study we applied EMD to analyse burst-suppression (BS) in the human EEG during induction of general anaesthesia (GA) with propofol. BS is a state characterised by cyclic changes between significant depression of brain activity and hyper-active bursts with variable duration, amplitude, and waveform shape. BS arises after induction into deep general anaesthesia after an intravenous bolus of general anaesthetics. Here we studied the behaviour of BS using the burst-suppression ratio (BSR).Results. Comparing correlations between EEG and IMF BSRs, we determined BSR was driven mainly by alpha activity. BSRs for different spectral components (IMFs 1-4) showed differing rates of return to baseline after the end of BS in EEG, indicating BS might differentially impair neural generators of low-frequency EEG oscillations and thalamocortical functional connectivity.Conclusion. Studying BS using EMD represents a novel form of analysis with the potential to elucidate neurophysiological mechanisms of this state and its impact on post-operative patient prognosis.

Download Full-text