Low-amplitude craniofacial EMG power spectral density and 3D muscle reconstruction from MRI

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles

Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.

International audienceElectroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. In this paper, realistic spike EEG signals are simulated from the activation of a 5 cm2 epileptic patch in the left superior temporal gyrus. Background activities and real muscle artifacts are then added to the simulated data. We compare the efficiency of Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Blind Source Separation based on Canonical Correlation Analysis (BSS-CCA) to remove muscle artifacts from the EEG signals. The quantitative comparison indicates that the EMD approach exhibits a better performance than ICA and BSS-CCA, especially in the case of very low Signal to Noise Ratio (SNR)

A Novel Method for Reducing the Effect of Tonic Muscle Activity on the Gamma Band of the Scalp EEG

Neural oscillations in the gamma band are of increasing interest, but separating them from myogenic electrical activity has proved difficult. A novel algorithm has been developed to reduce the effect of tonic scalp and neck muscle activity on the gamma band of the EEG. This uses mathematical modelling to fit individual muscle spikes and then subtracts them from the data. The method was applied to the detection of motor associated gamma in two separate groups of eight subjects using different sampling rates. A reproducible increase in high gamma (65–85 Hz) magnitude occurred immediately after the motor action in the left central area (p = 0.02 and p = 0.0002 for the two cohorts with individually optimized algorithm parameters, compared to p = 0.03 and p = 0.16 before correction). Whilst the magnitude of this event-related gamma synchronisation was not reduced by the application of the EMG reduction algorithm, the baseline left central gamma magnitude was significantly reduced by an average of 23 % with a faster sampling rate (p < 0.05). In comparison, at left and right temporo-parietal locations the gamma amplitude was reduced by 60 and 54 % respectively (p < 0.05). The reduction of EMG contamination by fitting and subtraction of individual spikes shows promise as a method of improving the signal to noise ratio of high frequency neural oscillations in scalp EEG

The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data

Word production begins with high-Gamma automatic linguistic processing functions followed by speech motor planning and articulation. Phonetic properties are processed in both linguistic and motor stages of word production. Four phonetically dissimilar phonemic structures “BA”, “FO”, “LE”, and “RY” were chosen as covert speech tasks. Ten neurologically healthy volunteers with the age range of 21–33 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. Initially, one-second trials were used, which contained linguistic and motor imagery activities. The four-class true positive rate was calculated. In the next stage, 312 ms trials were used to exclude covert articulation from analysis. By eliminating the covert articulation stage, the four-class grand average classification accuracy dropped from 96.4% to 94.5%. The most valuable features emerge after Auditory cue recognition (~100 ms post onset), and within the 70–128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke’s area (linked to Phonological code retrieval), the right IFG, and Broca’s area (linked to syllabification). Alpha and Beta band oscillations associated with motor imagery do not contain enough information to fully reflect the complexity of speech movements. Over 90% of the most class-dependent features were in the 30-128 Hz range, even during the covert articulation stage. As a result, compared to linguistic functions, the contribution of motor imagery of articulation in class separability of covert speech tasks from EEG data is negligible

Electroencefalografía en Cuidados Críticos

La electroencefalografía es la ciencia que estudia mediante un registro gráfico, en papel o digital, la actividad eléctrica generada por el córtex cerebral, en tiempo real, presentando una resolución temporal entre 2-4 mseg. Se registra mediante electrodos colocados en cuero cabelludo. Normalmente se utiliza el sistema internacional 10-20 para obtener una adecuada localización topográfica de las señales generadas en el córtex. La señal obtenida, corresponde a la suma de los potenciales de acción postsinápticos de las neuronas piramidales de la corteza cerebral. Esta señal es amplificada por diversos equipos informáticos, y tiene una correlación espacial dada por los distintos electrodos. &nbsp;Mediante el EEG, se puede obtener información localizada, y en tiempo real de distintos procesos que alteran la función normal de la corteza cerebral, ya sean focos epileptógenos, lesiones focales, procesos isquémicos o metabólicos. Además permite evaluar el nivel de consciencia, y sus distintas fluctuaciones, la reactividad cortical a diversos estímulos. Tiene un valor pronóstico en función de los distintos patrones registrados y permite evaluar el ciclo vigilia-sueño

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the wave form when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.Peer reviewe