We heard your wishes and are happy to announce that we have partnered with our good friends at CGX to be able to offer you a high-grade, dry electrode, wearable headset.
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.
In the past years there has been an increased need and demand for real-time access to EEG data for purposes of quality control, parametrization, features extraction and online feedback. In this article, I will explore the concept of remote data access (RDA) and demonstrate how to access EEG data in real-time from our amplifiers.
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.
We integrated concurrent real-time fMRI (rtfMRI) and electroencephalography (EEG) data on commercial MRI and EEG equipment. We also report a proof-of-concept experiment using simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). With this approach participants receive information about their electrophysiological (EEG) and hemodynamic (BOLD fMRI) activity in real-time, and volitionally regulate their own brain activity.
