This research combines mobile EEG and augmented reality (AR) to study cognition. Participants completed three face inversion tasks: (1) computer-based, (2) mobile EEG with photographs, (3) mobile EEG with AR. We find face inversion effects in all tasks, concluding that mobile EEG and AR is a promising approach for research.
Until recently, there has been little information about EEG data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we measure the relative heating of a MB protocol compared with a standard single-band (SB) protocol considered to be safe. We also evaluated EEG quality recorded concurrently with the MB protocol on humans.
We are happy to inform you that the new BrainVision Analyzer 2.2.2 has been released. We are also excited about adding a completely new transformation, the CWL Regression for EEG-fMRI data correction. Let me take you on a tour of the new features and enhancements so that you can download Analyzer 2.2.2 from our website today and get started.
So far, this series covered hyperscanning with BrainAmp amplifiers as well as with CGX Quick headsets. Part 3 introduces a more general approach, which is based on LabStreamingLayer (LSL).
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.
We recently investigated how musical partners resolve unmeasured expressive silences in musical interaction. Partners resolved shorter silences more synchronously than longer silences; partners also displayed enhanced neural markers of motor preparation for shorter relative to longer silences. Thus, shorter silences in interaction may facilitate interpersonal coordination.
We are excited to let you know that Carbon Wire Loops (CWL) for the BrainCap MR are now available to order. This solution is extremely helpful for the correction of motion related artefacts.
EEG signals acquired in parallel to fMRI data require the handling of artifacts in the EEG dataset due to electrode motion, for example, by blood pulsation. Using carbon wire loops placed on EEG caps allows you to directly measure these artifacts and subsequently correct for them efficiently using a regression-based approach.
This aEEG-TMSrticle walks you through the collection of neuro-navigated simultaneously obtained Transcranial Magnetic Stimulation (TMS) evoked EEG (TMS-EEG) data to investigate network-specific TMS-induced EEG activations, as in Ozdemir et al, 2020 (PNAS), focusing specifically on the methods of TMS-EEG data collection and analysis.
Curious about combining EEG and fNIRS for your stationary, lab-based research? If the answer is yes, then check out our step-by-step guide on stationary EEG-fNIRS setup with actiCHamp Plus, actiCAP slim/snap, and NIRScout.
