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OBJECTIVE: High-density EEG recording offers increased spatial resolution but requires careful consideration of how the density of electrodes affects the potentials being measured. Power differences as a function of electrode density and electrolyte spreading were examined and a method for correcting these differences was tested. METHODS: Separate EEG recordings from 8 participants were made using a high-density electrode net, first with 6 of 128 electrodes active followed by recordings with all electrodes active. For a subset of 4 participants measurements were counterbalanced with recordings made in the reversed order by drying the hair after the high-density recordings and using a fresh dry electrode net of the same size for the low-density recordings. Mean power values over 6 resting eyes open/closed EEG recordings at the 6 active electrodes common to both recording conditions were compared. Evidence for possible electrolyte spreading or bridging between electrodes was acquired by computing Hjorth electrical distances. Spherical spline interpolation was tested for correcting power values at electrodes affected by electrolyte spreading for these participants and for a subset of participants from a larger previous study. RESULTS: For both the complete set and the counterbalanced subset, significant decreases in power at the 6 common electrodes for the high-density recordings were observed across the range of the standard EEG bands (1-44 Hz). The number of bridges or amount of electrolyte spreading towards the reference electrode as evidenced by small Hjorth electrical distances served as a predictor of this power decrease. Spherical spline interpolation increased the power values at electrodes affected by electrolyte spreading and by a significant amount for the larger number of participants in the second group. CONCLUSIONS: Understanding signal effects caused by closely spaced electrodes, detecting electrolyte spreading and correcting its effects are important considerations for high-density EEG recordings. A combination of scalp maps of power density and plots of small Hjorth electrical distances can be used to identify electrodes affected by electrolyte spreading. Interpolation using spherical splines offers a method for correcting the potentials measured at these electrodes.
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Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.
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EEG and EEG source-estimation are susceptible to electromyographic artifacts (EMG) generated by the cranial muscles. EMG can mask genuine effects or masquerade as a legitimate effect-even in low frequencies, such as alpha (8-13 Hz). Although regression-based correction has been used previously, only cursory attempts at validation exist, and the utility for source-localized data is unknown. To address this, EEG was recorded from 17 participants while neurogenic and myogenic activity were factorially varied. We assessed the sensitivity and specificity of four regression-based techniques: between-subjects, between-subjects using difference-scores, within-subjects condition-wise, and within-subject epoch-wise on the scalp and in data modeled using the LORETA algorithm. Although within-subject epoch-wise showed superior performance on the scalp, no technique succeeded in the source-space. Aside from validating the novel epoch-wise methods on the scalp, we highlight methods requiring further development.
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