Detection of muscle artefact in the normal human awake EEG

Velde, van de M Maarten; Erp, van Mg; Cluitmans, Pjm Pierre

doi:10.1016/s0013-4694(98)00052-2

Cited by 44 publications

(39 citation statements)

References 18 publications

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“…The typical duration of an eye blink is 200-400 ms, and its spectral signature spans the delta and theta range (101,102), with most of the energy residing below 5 Hz. Muscle artifacts arise from the fluttering of the electrodes in the vicinity of active neck and face muscle groups and span the frequency range from the beta band up to Ϸ500 Hz (103,104). The spectral characteristics of these two contaminants (eye blinks and muscle artifacts) have no overlap with the frequency bands investigated here.…”

Section: Methodsmentioning

confidence: 86%

The phi complex as a neuromarker of human social coordination

Tognoli

Lagarde²,

DeGuzman³

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

381

340

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Many social interactions rely upon mutual information exchange: one member of a pair changes in response to the other while at the same time producing actions that alter the behavior of the other. However, little is known about how such social processes are integrated in the brain. Here, we used a specially designed dualelectroencephalogram system and the conceptual framework of coordination dynamics to identify neural signatures of effective, real-time coordination between people and its breakdown or absence. High-resolution spectral analysis of electrical brain activity before and during visually mediated social coordination revealed a marked depression in occipital alpha and rolandic mu rhythms during social interaction that was independent of whether behavior was coordinated or not. In contrast, a pair of oscillatory components (phi 1 and phi2) located above right centroparietal cortex distinguished effective from ineffective coordination: increase of phi 1 favored independent behavior and increase of phi 2 favored coordinated behavior. The topography of the phi complex is consistent with neuroanatomical sources within the human mirror neuron system. A plausible mechanism is that the phi complex reflects the influence of the other on a person's ongoing behavior, with phi 1 expressing the inhibition of the human mirror neuron system and phi 2 its enhancement.brain oscillations ͉ electroencephalography ͉ mirror neuron system ͉ phi rhythm ͉ coordination dynamics T wo anatomically overlapping yet functionally distinct systems in the brain have been identified when we interact with others. The first, historically called the motor preparation system, consists of cortical circuitry that includes the premotor cortex, the supplementary motor area, and parts of the inferior parietal cortex (1). This system is deemed responsible for implementing the intention to realize one's own actions (2, 3). The second, the mirror-neuron system (4, 5), allows for the actions of others to be perceived (6), embodied (7), understood (8, 9), and appropriated (10) by our own motor system. Its main components are the inferior parietal sulcus, the premotor cortex (5,11,12), and the superior temporal sulcus (STS) (although the motor properties of STS neurons coactivated during observation and execution are presently the subject of some debate; see ref. 6). In evolutionary terms, the mirror-neuron system may facilitate important functions of skill learning, language acquisition, everyday joint action, and interpersonal coordination (13). A common viewpoint (5, 14) is that the mirror-neuron system is inactive most of the time but is activated upon request. Research on pathological imitation (15) suggests a further possibility, namely, that the mirror-neuron system is constantly available for use but is actively suppressed by inhibition (16).Neurophysiological studies of the influence of one person's actions on another have so far assessed the behavioral acts of pairs of individuals one at a time, i.e., one acts while the other observes; or o...

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Section: Methodsmentioning

confidence: 86%

The phi complex as a neuromarker of human social coordination

Tognoli

Lagarde²,

DeGuzman³

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

381

340

View full text Add to dashboard Cite

show abstract

“…This type of volume conduction would constitute the 'inverse' of the volume conduction from intracranial-to-extracranial, the latter underlying the measurable scalp EEG signals in healthy subjects (Fuchs et al, 2007;Holsheimer and Feenstra, 1977;van den Broek et al, 1998). (2) In the special case of epilepsy patients with subdurally implanted electrodes as investigated in the present study, volume conduction might also occur through the bone defects resulting from the craniotomy carried out for electrode implantation.…”

Section: Discussionmentioning

confidence: 94%

“…Further investigations will, however, be required to clarify this issue, possibly using electrical source localization techniques similar to those that were developed for analyzing the opposite case, i.e. volume conduction from within the scull to the outside (Ball et al, 1999;Fuchs et al, 2007;Holsheimer and Feenstra, 1977;van den Broek et al, 1998), in particular using finite element method (FEM) volume conductor models (Zhang et al, 2006) for accurate modeling of the burr holes.…”

Section: Discussionmentioning

confidence: 99%

“…Sources of artifact contamination in non-invasive EEG include blinks, eye movements such as saccades or also micro-saccades, head movements, and electromyographic (EMG) activity from muscles close to the recording sites (Crespo-Garcia et al, 2008;Croft and Barry, 2000;Fitzgibbon et al, 2007;Ghandeharion and Erfanian, 2006;Joynt, 1959;Li and Principe, 2006;ter Meulen et al, 2006;van de Velde et al, 1998;Yuval-Greenberg et al, 2008). A principal advantage of invasive EEG that has been repeatedly NeuroImage 46 (2009) [708][709][710][711][712][713][714][715][716] ⁎ Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

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Signal quality of simultaneously recorded invasive and non-invasive EEG

et al. 2009

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Both invasive and non-invasive electroencephalographic (EEG) recordings from the human brain have an increasingly important role in neuroscience research and are candidate modalities for medical brainmachine interfacing. It is often assumed that the major artifacts that compromise non-invasive EEG, such as caused by blinks and eye movement, are absent in invasive EEG recordings. Quantitative investigations on the signal quality of simultaneously recorded invasive and non-invasive EEG in terms of artifact contamination are, however, lacking. Here we compared blink related artifacts in non-invasive and invasive EEG, simultaneously recorded from prefrontal and motor cortical regions using an approach suitable for detection of small artifact contamination. As expected, we find blinks to cause pronounced artifacts in noninvasive EEG both above prefrontal and motor cortical regions. Unexpectedly, significant blink related artifacts were also found in the invasive recordings, in particular in the prefrontal region. Computing a ratio of artifact amplitude to the amplitude of ongoing brain activity, we find that the signal quality of invasive EEG is 20 to above 100 times better than that of simultaneously obtained non-invasive EEG. Thus, while our findings indicate that ocular artifacts do exist in invasive recordings, they also highlight the much better signal quality of invasive compared to non-invasive EEG data. Our findings suggest that blinks should be taken into account in the experimental design of ECoG studies, particularly when event related potentials in fronto-anterior brain regions are analyzed. Moreover, our results encourage the application of techniques for reducing ocular artifacts to further optimize the signal quality of invasive EEG.

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“…Since the interesting EEG frequencies are located below this limit, each EEG trial is filtered between 1 and 40 Hz. Muscular movements induce relatively high frequency (above 25 Hz), high amplitude components (muscle artefacts) in electrodes located in the motor area [4]. We set a power threshold (determined during the training phase) on the frequency band above 25 Hz, in order to detect such artefacts.…”

Section: Eeg Trial Preprocessingmentioning

confidence: 99%

Direct brain-computer communication with user rewarding mechanism

Garcia

Ebrahimi

Vesin

et al. 2003

IEEE International Symposium on Information Theory, 2003. Proceedings.

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An electroencephalogram (EEG) based Brain ComputerInterface (BCI) and its main components are presented. The BCI operation model is established on the basis of a three-state machine where the direct transitions from the neutral to the active state are not allowed. A transition state is introduced in order to make the user confirm his intents through latency. The latency is a dynamic parameter that decreases when the user achieves an acceptable level of performance. EEG signals are classified in the space generated by their correlative time-frequency-space representation (CTFSR). Nonlinear separation in this space is achieved by using the support vector machine approach. An adequate kernel design makes the computations take place in the temporal domain. Good efficiency is obtained after five experiment sessions by two subjects.

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Detection of muscle artefact in the normal human awake EEG

Cited by 44 publications

References 18 publications

The phi complex as a neuromarker of human social coordination

The phi complex as a neuromarker of human social coordination

Signal quality of simultaneously recorded invasive and non-invasive EEG

Direct brain-computer communication with user rewarding mechanism

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