In this article, based on our current work, we compared transcranial evoked potentials recorded with active and passive electrodes. Signals obtained with the two methods did not statistically differ in amplitude and topography, and showed a high degree of similarity across the scalp. We conclude that active electrodes are a viable solution for studies combining transcranial magnetic stimulation and electroencephalography.
EEG-assisted retrospective motion correction for fMRI (E-REMCOR) and automated implementation (aE-REMCOR)
Electroencephalography (EEG) concurrently acquired with fMRI provides high temporal resolution information about brain activity as well as subject head movement. We introduced an EEG-assisted retrospective motion correction (E-REMCOR) method that utilizes EEG data to correct for head movements in fMRI on a slice-by-slice basis and substantially improves the quality of the data. To further enhance the usability of E-REMCOR, especially for the large-scale EEG-fMRI studies, we developed an automatic and improved implementation of E-REMCOR, referred as aE-REMCOR.
This user research summary is based on the article “Acute Biphasic Effects of Ayahuasca“. Ayahuasca is an amerindian psychoactive sacrament used worldwide. Neuroscience studies have shown contradictory results regarding its effects in the brain. Combining EEG, plasma samples and robust statistics, we’ve uncovered biphasic effects in the brain which are related to many psychoactive compounds, not only N,N-dimethyltryptamine (DMT), as previously proposed.
Motor imagery (MI) combined with neurofeedback has been suggested as a promising rehabilitation approach for paralyzed individuals. EEG based MI feedback is particularly promising for therapeutic applications. Yet whether EEG feedback indeed targets specific sensorimotor activation patterns cannot unambiguously inferred from EEG alone. This article demonstrates that online correction of gradient artifacts and ballistocardiogram artifacts enables reliable MI EEG feedback inside the MRI scanner.
For most researchers, Independent Component Analysis (ICA) might be equivalent with a black box, magically cleaning your EEG data. In this article, we uncover the theoretical background and requirements of ICA and present its implementation in BrainVision Analyzer. We will provide core insights into the method, allowing you to confidently integrate ICA into your data processing pipelines.