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May 14, 2020

Connectivity Layer and Other Features of EEG Headsets Explained

When choosing an electroencephalogram (EEG) device it is important to find the best possible performance by balancing device features and value. Once all the signal related features (those which apply to both sensor and amplifier stages) have been addressed, we will discuss other important characteristics such as connectivity, power, autonomy, data format, etc.

May 5, 2020

This Is How Eye Tracking Technology Works

Eye tracking has become an invaluable tool for understanding attention, visual behavior and human behavior in a number of diverse fields, from psychology, neurophysiology, user experience, market research, etc. The technology can also be used for medical analysis and screening, and it provides a new method of interaction. Historically, eye tracking systems were invasive and immobile, and therefore useful only in very limited experiments.

eye that is being recorded by an eye trracking technology

April 30, 2020

EEG Electrode Placement: Fixed vs. Variable

The market for neurotechnology devices is growing, with many new tools available. Finding the right EEG headset can be a challenge. There are many features that will influence this decision, but a very important one is whether the EEG has a fixed or variable sensor layout. Variable layouts will give you flexibility at the cost of usability, and fixed layouts the opposite. We explain here the pros and cons of each version.

girl wih a variable EEG electrode placement

April 23, 2020

How Deep Learning is changing machine learning AI in EEG signal processing

EEG systems capture information about many different aspects of our cognition, behavior, and emotions. The technology not only helps to study the brain, but also has applications in health, in affective and emotional monitoring, and in human improvement. However, EEG data is not easy to interpret: it has a lot of noise, varies significantly between individuals and, even for the same person, changes substantially over time.

eeg data processing with ai, deeplearning, machine learning

April 17, 2020

All about EEG artifacts and filtering tools

One of the main concerns when dealing with electroencephalographic signals (EEG) is assuring that we record clean data with a high signal to noise ratio. The EEG signal amplitude is in the microvolts range and it is easily contaminated with noise, known as “artifacts”, which need to be filtered from the neural processes to keep the valuable information we need for our applications. We review in this post different EEG artifacts and the main tools and techniques to remove them.

April 10, 2020

Introduction to Implicit Bias Tests: IAT and Priming implicit tests

Implicit measures became very popular years ago by the academics, and many researchers employed them to analyze biases in racial groups, gender, sexuality, age, and religion, as well as to assess self-esteem in clinical psychology. Today, other applications in market research have emerged to gather information on implicit preferences of products, brands, politicians, and celebrities, and to reveal which attributes are more inherently associated with these concepts.

a participant performing an implicit bias test

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