Tracking and understanding the mental state of populations around the world is important for understanding how we are faring as a collective humanity and determining…
Self-report survey has been one of the main tools in the study of human populations in matters of mind across numerous disciplines from Sociology to…
Here we explain how and why the MHQ scale is constructed including addressing common questions such as why the MHQ uses a negative-positive scale as…
What are the common approaches to measuring depth of anesthesia in clinical application and how do we evaluate the algorithms used? Monitoring the depth of…
How do you compare and select the best Bayesian model in Dynamic Causal Modeling or DCM of EEG data? In the previous two blogs we…
This post discusses the core components of neural mass models and Bayesian inference in DCM applied to EEG.
Dynamic Causal Modeling (DCM) takes a probabilistic Bayesian framework to infer effective or causal connectivity, essentially to model how a stimulus would influence the connectivity…
A Bayesian framework, one that works with conditional probabilities, has numerous applications in Neuroimaging in general and in EEG specifically. But first, a primer on Bayes theorem and how it works.
The blood may hold many clues to damage and degeneration in the brain. New findings bring a blood test for brain disorders closer to reality….
Dry electrodes have some clear advantages but how does their signal quality compare to wet electrodes? Choosing one over the other may be a tradeoff between time, signal quality and stability.
EEG recording technology remains similar in principle since its first use in 1923. However, a vast array of electrode types, both wet and dry, are…
The lymphatic system plays a key role in waste removal and immune function. Until 2012 it was thought that not such waste removal system existed…
Optogenetics have been used for bi-directional BCI in mice but is unsuitable for freely moving subjects due to its tethered optical fibers or fixed imaging…
Performing a motor task requires a feedback loop that informs the brain of success of each motor action. For sensory impaired this means brain computer…
SSVEP-based spellers, a common BCI application, have improved in information transfer rates over the years. In the previous post we discussed the fundamentals of SSVEP-based…
Steady State Visual Evoked Potentials uses different frequencies of visual stimulii to distinguish between objects in BCI applications and have relatively high information transfer rates….
The cerebellum has four times as many cells as the neocortex and possible roles in everything from movement to cognition and emotion but is much…
What is involved in a Brain Machine Interface (BMI; also called Brain Computer Interface or BCI) set-up? Here is a primer on paradigms for BMI…
This is a primer on fNIRS, what it does, the technical advances, challenges and potential applications. With all the recent publicity around Kernel’s new fNIRS…
The slope that represents the decay of the EEG power spectrum carries important information about the signal that can eliminate many of the inconsistencies of…
There is a great deal of hype about neuroimaging technologies being able to read our thoughts. And yet, we don’t really know what a thought…
LCMV Beamformers are a type of beamformer algorithm used source estimation from EEG data. In the previous blog post we saw how beamformers can be…
The Brain Imaging Data Structure (BIDS) project is an effort to create data standards for accessibility, usability and reproducibility of neuroimaging data. Initially developed for…
Beamformers, a technique adapted from radar applications, are a type of spatial filtering approach to solving the inverse problem in EEG and MEG. Here are…
The brain develops and morphs across the lifespan based on experience. Two talks discuss the impact of our behaviors from physical exercise to cell phone…
EEG Microstates are generally identified using clustering algorithms and results can vary depending on choice of algorithm and parameters. In the last post I introduced…
Where is knowledge stored within the brain and living systems? The concept of monitor-and-act units provides a framework for understanding this. In my last post…
EEG microstates represent a dynamical view of how the spatial distribution of the electric potential on the scalp changes over time. Microstates typically last 80-100…
Here are three tutorials on common methods, challenges and pitfalls in the analysis of EEG for those who missed the symposium last year covering issues…
Transfer entropy is a way to estimate interdependencies between two time series that is sensitive to both linear and nonlinear interactions so a useful measure…
One of the key challenges of anesthesia is assessing the state of consciousness or depth of anesthesia of the person through the process of surgery. …
Granger causality is an estimation of causality in time series that depends on linear methods. Does it have relevance to the nonlinear, non-stationary EEG signal…
Often artifact removal means throwing out a lot of valuable signal. Is it worthwhile to throw the baby out with the bathwater? The EEG signal…
Here I discuss the concept of an adaptive traverses and how this might explain the link between genes and behavior. Your DNA knows things. It…
How insights from the way the brain learns can be used to design adaptive learning mechanisms for AI that include both phylogenetic and ontogenetic aspects….
Methodological variability is a challenge in the EEG field at various levels. This video provides a view of these challenges in four major areas along…
EEGLAB is an open source platform running on Matlab with a large set of functions for analyzing the EEG signal that are constantly being updatd. …
The Temple University Hospital (TUH) Corpus with over 30,000 EEG records and associated clinical reports is one of the largest open EEG data repositories available….
Intelligence is an adaptive process that requires an environment on which it can act. To uncover if neurons have independent intelligence requires closed loop experiments…
If individual neurons were as intelligent as little minions, how would we know? The experimental paradigms today assume they are not, what should we change…
Wavelet transforms can be used to compute coherence in the EEG and estimate dynamic interactions. Here is a primer on how to do this. Analogous…
We all like new starts and the new year is a great time to revamp some things that maybe aren’t working well or could work…