ORCID Profile
0000-0001-8364-6509
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1016/J.JNEUMETH.2018.01.004
Abstract: Contamination of scalp measurement by tonic muscle artefacts, even in resting positions, is an unavoidable issue in EEG recording. These artefacts add significant energy to the recorded signals, particularly at high frequencies. To enable reliable interpretation of subcortical brain activity, it is necessary to detect and discard this contamination. We introduce a new automatic muscle-removal approach based on the traditional Blind Source Separation-Canonical Correlation Analysis (BSS-CCA) method and the spectral slope of its components. We show that CCA-based muscle-removal methods can discriminate between signals with high correlation coefficients (brain, mains artefact) and signals with low correlation coefficients (white noise, muscle). We also show that typical BSS-CCA components are not purely from one source, but are mixtures from multiple sources, limiting the performance of BSS-CCA in artefact removal. We demonstrate, using our paralysis dataset, improved performance using BSS-CCA followed by spectral-slope rejection. This muscle removal approach can reduce high-frequency muscle contamination of EEG, especially at peripheral channels, while preserving steady-state brain responses in cognitive tasks. This approach is automatic and can be applied on any s le of data easily. The results show its performance is comparable with the ICA method in removing muscle contamination and has significantly lower computational complexity. We identify limitations of the traditional BSS-CCA approach to artefact removal in EEG, propose and test an extension based on spectral slope that makes it automatic and improves its performance, and results in performance comparable to competitors such as ICA-based artefact removal.
Publisher: IEEE
Date: 05-2012
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 03-2016
DOI: 10.1016/J.CLINPH.2015.12.009
Abstract: Validate independent component analysis (ICA) for removal of EMG contamination from EEG, and demonstrate a heuristic, based on the gradient of EEG spectra (slope of graph of log EEG power vs log frequency, 7-70 Hz) from paralysed awake humans, to automatically identify and remove components that are predominantly EMG. We studied the gradient of EMG-free EEG spectra to quantitatively inform the choice of threshold. Then, pre-existing EEG from 3 disparate experimental groups was examined before and after applying the heuristic to validate that the heuristic preserved neurogenic activity (Berger effect, auditory odd ball, visual and auditory steady state responses). (1) ICA-based EMG removal diminished EMG contamination up to approximately 50 Hz, (2) residual EMG contamination using automatic selection was similar to manual selection, and (3) task-induced cortical activity remained, was enhanced, or was revealed using the ICA-based methodology. This study further validates ICA as a powerful technique for separating and removing myogenic signals from EEG. Automatic processing based on spectral gradients to exclude EMG-containing components is a conceptually simple and valid technique. This study strengthens ICA as a technique to remove EMG contamination from EEG whilst preserving neurogenic activity to 50 Hz.
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-2666-9.CH016
Abstract: Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brainwaves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of such system through providing a better sub-set of electrodes or features, reducing the required training time of the classifiers, reducing the noise to signal ratio, and so on. This chapter provides a survey on some of the commonly used EA methods in EEG study.
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 09-2015
DOI: 10.1016/J.IJPSYCHO.2014.10.006
Abstract: The serious impact of electromyogram (EMG) contamination of electroencephalogram (EEG) is well recognised. The objective of this research is to demonstrate that combining independent component analysis with the surface Laplacian can eliminate EMG contamination of the EEG, and to validate that this processing does not degrade expected neurogenic signals. The method involves sequential application of ICA, using a manual procedure to identify and discard EMG components, followed by the surface Laplacian. The extent of decontamination is quantified by comparing processed EEG with EMG-free data that was recorded during pharmacologically induced neuromuscular paralysis. The combination of the ICA procedure and the surface Laplacian, with a flexible spherical spline, results in a strong suppression of EMG contamination at all scalp sites and frequencies. Furthermore, the ICA and surface Laplacian procedure does not impair the detection of well-known, cerebral responses alpha activity with eyes-closed ERP components (N1, P2) in response to an auditory oddball task and steady state responses to photic and auditory stimulation. Finally, more flexible spherical splines increase the suppression of EMG by the surface Laplacian. We postulate this is due to ICA enabling the removal of local muscle sources of EMG contamination and the Laplacian transform being insensitive to distant (postural) muscle EMG contamination.
Publisher: BMJ
Date: 2003
DOI: 10.1136/JNNP.74.1.51
Abstract: Gamma oscillations (30-100 Hz gamma electroencephalographic (EEG) activity) correlate with high frequency synchronous rhythmic bursting in assemblies of cerebral neurons participating in aspects of consciousness. Previous studies in a kainic acid animal model of epilepsy revealed increased intensity of gamma rhythms in background EEG preceding epileptiform discharges, leading the authors to test for intensified gamma EEG in humans with epilepsy. 64 channel cortical EEG were recorded from 10 people with primary generalised epilepsy, 11 with partial epilepsy, and 20 controls during a quiescent mental state. Using standard methods of EEG analysis the strength of EEG rhythms (fast Fourier transformation) was quantified and the strengths of rhythms in the patient groups compared with with controls by unpaired t test at 1 Hz intervals from 1 Hz to 100 Hz. In patients with generalised epilepsy, there was a threefold to sevenfold increase in power of gamma EEG between 30 Hz and 100 Hz (p<0.01). Analysis of three unmedicated patients with primary generalised epilepsies revealed an additional 10-fold narrow band increase of power around 35 Hz-40 Hz (p<0.0001). There were no corresponding changes in patients with partial epilepsy. Increased gamma EEG is probably a marker of the underlying ion channel or neurotransmitter receptor dysfunction in primary generalised epilepsies and may also be a pathophysiological prerequisite for the development of seizures. The finding provides a new diagnostic approach and also links the pathophysiology of generalised epilepsies to emerging concepts of neuronal correlates of consciousness.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IOP Publishing
Date: 28-05-2015
DOI: 10.1088/0967-3334/36/7/1469
Abstract: Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HI (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open (2) visual steady-state response (VSSR) (3) mismatch negativity (4) P300 and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HI . The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.
Publisher: Elsevier BV
Date: 08-2007
DOI: 10.1016/J.CLINPH.2007.04.027
Abstract: To identify the possible contribution of electromyogram (EMG) to scalp electroencephalogram (EEG) rhythms at rest and induced or evoked by cognitive tasks. Scalp EEG recordings were made on two subjects in presence and absence of complete neuromuscular blockade, sparing the dominant arm. The subjects undertook cognitive tasks in both states to allow direct comparison of electrical recordings. EEG rhythms in the paralysed state differed significantly compared with the unparalysed state, with 10- to 200-fold differences in the power of frequencies above 20 Hz during paralysis. Most of the scalp EEG recording above 20 Hz is of EMG origin. Previous studies measuring gamma EEG need to be re-evaluated. This has a significant impact on measurements of gamma rhythms from the scalp EEG in unparalysed humans. It is to be hoped that signal separation methods will be able to rectify this situation.
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 10-2001
DOI: 10.1016/S0167-8760(01)00164-7
Abstract: This study is an exploratory investigation of the regional timing of cortical activity associated with verbal working memory function. ERP activity was obtained from a single subject using a 124-channel sensor array during a task requiring the monitoring of imageable words for occasional targets. Distributed cortical activity was estimated every 2.5 ms with high spatial resolution using real head, boundary element modelling of non-target activity. High-resolution structural MRI was used for segmentation of tissue boundaries and co-registration to the scalp electrode array. The inverse solution was constrained to the cortical surface. Cortical activity was observed in regions commonly associated with verbal working memory function. This included: the occipital pole (early visual processing) the superior temporal and inferior parietal gyrus bilaterally and the left angular gyrus (visual and phonological word processing) the dorsal lateral occipital gyrus (spatial processing) and aspects of the bilateral superior parietal lobe (imagery and episodic verbal memory). Activity was also observed in lateral and superior prefrontal regions associated with working memory control of sensorimotor processes. The pattern of cortical activity was relatively stable over time, with variations in the extent and litude of contributing local source activations. By contrast, the pattern of concomitant scalp topography varied considerably over time, reflecting the linear summation effects of volume conduction that often confound dipolar source modelling.
Publisher: Elsevier BV
Date: 08-2004
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-2007
Publisher: Wiley
Date: 29-10-2003
DOI: 10.1046/J.1528-1157.2003.20103.X
Abstract: We previously revealed an interictal increase in intensity of EEG rhythms during quiescent mental activity in the 30- to 100-Hz frequency (gamma) range in primary generalized epilepsy (PGE). We have evidence that there is induction of gamma EEG in normal subjects in response to controlled mental activity. Here we test whether mental tasks further augment interictal gamma oscillations in people with PGE. We recorded interictal EEG from patients with PGE and partial epilepsy and compared EEG power spectral responses (increases over resting) during mental tasks. In partial epilepsy, mental tasks (except for alternating checkerboard visual stimulation) induced 1.5- to 2.5-fold increases in power of gamma EEG. In generalized epilepsy, generalized increases of 1.5-fold in gamma EEG were induced by only two mental tasks (reading and subtraction), and enhancement of 1- to 1.5-fold in the remaining six (checkerboard, expectancy, music, learning, recalling, and a video). Gamma EEG is less responsive to mental activation in PGE than in partial epilepsy, confirming an abnormality in gamma mechanisms in PGE. Our findings also provide a possible mechanistic link between mental activity and seizures in reading- and arithmetic-induced seizures.
Publisher: Elsevier BV
Date: 12-2016
DOI: 10.1016/J.IJPSYCHO.2016.09.020
Abstract: Meditative techniques aim for and meditators report states of mental alertness and focus, concurrent with physical and emotional calm. We aimed to determine the electroencephalographic (EEG) correlates of five states of Buddhist concentrative meditation, particularly addressing a correlation with meditative level. We studied 12 meditators and 12 pair-matched meditation-naïve participants using high-resolution scalp-recorded EEG. To maximise reduction of EMG, data were pre-processed using independent component analysis and surface Laplacian transformed data. Two non-meditative and five meditative states were used: resting baseline, mind-wandering, absorptions 1, 2, 3, 4 and 5 (corresponding to four levels of absorption and an absorption with a different object of focus, otherwise equivalent to level 4 these five meditative states produce repeatable, distinctly different experiences for experienced meditators). The experimental protocol required participants to experience the states in the order listed above, followed immediately by the reverse. We then calculated EEG power in standard frequency bands from 1 to 80Hz. We observed decreases of central scalp beta (13-25Hz), and central low gamma (25-48Hz) power in meditators during deeper absorptions. In contrast, we identified increases in frontal midline and temporo-parietal theta power in meditators, again, during deeper absorptions. Alpha activity was increased over all meditative states, not depth-related. This study demonstrates that the subjective experiences of deepening meditation partially correspond to measures of EEG. Our results are in accord with prior studies on non-graded meditative states. These results are also consistent with increased theta correlating with tightness of focus, and reduced beta/gamma with the desynchronization associated with enhanced alertness.
Publisher: Elsevier BV
Date: 08-2017
DOI: 10.1016/J.JNEUMETH.2017.06.011
Abstract: Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed. In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level. Using a generic volume conduction model of the head, which includes three layers (brain, skull, and scalp), and sLORETA, we estimated time-series of sources distributed within the brain and scalp. The sources within the scalp were considered to be muscle and discarded in forward modelling. (1) The method reduced EMG contamination, more strongly at peripheral channels (2) task-induced cortical activity was retained or revealed after removing putative muscle activity. This approach can decrease tonic muscle contamination in scalp measurements without relying on time-consuming processing of expensive MRI data. In addition, it is competitive to ICA in muscle reduction and can be reliably applied on any length of recorded data that captures the dynamics of the signals of interest. This study suggests that sLORETA can be used as a method to quantitate cranial muscle activity and reduce its contamination at sensor level.
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 20-02-2009
DOI: 10.1007/S10548-009-0081-X
Abstract: We recorded scalp electrical activity before and after full neuro-muscular paralysis in 5 volunteers and determined differences due to elimination of muscular activity on several standard applications of EEG. Due to paralysis, there were reductions in 'noisiness' of the standard scalp recordings which were maximal over the peripheral scalp, not explained by abolition of movement artefact, and best accounted for by sustained EMG activity in resting in iduals. There was a corresponding reduction in spectral power in the gamma range. In central leads, the extent of gamma frequency coherence during a non-time-locked mental task (1 s epochs) was reduced by paralysis, likely due to a reduction in gamma-frequency coherence in widely arising EMG signals. In a time-locked mental task (auditory oddball), evoked responses were qualitatively unaffected by paralysis but 3 of 4 induced gamma responses were obscured by EMG.
Publisher: IEEE
Date: 02-2014
Publisher: IEEE
Date: 06-2012
Publisher: Wiley
Date: 12-11-2002
DOI: 10.1002/HBM.10073
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.EPLEPSYRES.2012.12.009
Abstract: Studies of partial or generalized seizure pathophysiology often require the use of intact animals. Additionally, anesthesia may be required for ethical reasons or paralysis if instrumental measures require immobilization. We examined three commonly used injected anesthetic for their impact on seizures induced by three convulsant agents. We prepared rats, under pentobarbitone anesthesia (65 mg/kg) with a catheter, electrodes and a dural window, for later non-noxious experimentation. Three to seven days later, kainic acid (1.25 μg), picrotoxin (225 ng) or fluorocitrate (0.8 nmol) were injected intra-cortically in animals paralysed with succinylcholine, or anesthetised with pentobarbitone, urethane or fentanyl plus droperidol. We recorded EEG activity, the latencies to seizure discharges, the occurrence of spreading depressions and the presence of movements in response to the convulsants. Fentanyl plus droperidol was the only anesthetic agent permissive for seizure-discharges and spreading depressions. No significant differences in the time for seizure onset for fentanyl plus droperidol compared to paralyzed unanesthetised rats were seen for any of the convulsants (Student's t-test p>0.20). Movements during seizures as well as other drug-induced behaviors continued to be expressed during anesthesia. Fentanyl plus droperidol has useful properties as an anesthetic agent in studies of seizure induction with different convulsants.
Publisher: SPIE
Date: 22-12-2015
DOI: 10.1117/12.2209412
Publisher: Frontiers Media SA
Date: 2011
Publisher: IEEE
Date: 06-2012
Publisher: Elsevier BV
Date: 05-2008
DOI: 10.1016/J.CLINPH.2008.01.024
Abstract: Fast electrical rhythms in the gamma range (30-100Hz) in scalp (but not intracranial) recordings are predominantly due to electromyographic (EMG) activity. We hypothesized that increased EMG activity would be augmented by mental tasks in proportion to task difficulty and the requirement of these tasks for motor or visuo-motor output. EEG was recorded in 98 subjects whilst performing cognitive tasks and analysed to generate power spectra. In four other subjects, neuromuscular blockade was achieved pharmacologically providing EMG-free spectra of EEG at rest and during mental tasks. In comparison to the paralysed condition, power of scalp electrical recordings in the gamma range varied in distribution, being maximal adjacent to cranial or cervical musculature. There were non-significant changes in mean gamma range activity due to mental tasks in paralysed subjects. In normal subjects, increases in scalp electrical activity were observed during tasks, without relationship to task difficulty, but with tasks involving limb- or eye-movement having higher power. Electrical rhythms in the gamma frequency range recorded from the scalp are inducible by mental activity and are largely due to EMG un-related to cognitive effort. EMG varies with requirements for somatic or ocular movement more than task difficulty. Severe restrictions exist on utilizing scalp recordings for high frequency EEG.
Publisher: Elsevier BV
Date: 02-2017
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.COMPBIOMED.2019.103329
Abstract: In this paper, we perform the first comparison of a large variety of effective connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength of interdependence between time series are compared: information theoretic measures, model-based multivariate measures in the time and frequency domains, and phase-based measures. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well. Overall, the measure with the best performance in a variety of situations and with a low computational cost is conditional Granger causality. Partial Granger causality and multivariate Granger causality are also good measures, but their computational cost rises rapidly with the number of channels. Copula Granger causality can also be used reliably, but its computational cost rises rapidly with the number of data.
Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.COMPBIOMED.2018.12.005
Abstract: In neuroscience, there is considerable current interest in investigating the connections between different parts of the brain. EEG is one modality for examining brain function, with advantages such as high temporal resolution and low cost. Many measures of connectivity have been proposed, but which is the best measure to use? In this paper, we address part of this question: which measure is best able to detect connections that do exist, in the challenging situation of non-stationary and noisy data from nonlinear systems, like EEG. This requires knowledge of the true relationship between signals, hence we compare 26 measures of functional connectivity on simulated data (unidirectionally coupled Hénon maps, and simulated EEG). To determine whether synchrony is detected, surrogate data were generated and analysed, and a threshold determined from the surrogate ensemble. No measure performed best in all tested situations. The correlation and coherence measures performed best on stationary data with many s les. S-estimator, correntropy, mean-phase coherence (Hilbert), mutual information (kernel), nonlinear interdependence (S) and nonlinear interdependence (N) performed most reliably on non-stationary data with small to medium window sizes. Of these, correlation and S-estimator have execution times that scale slower with the number of channels and the number of s les.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sean Fitzgibbon.