Discover the Why of the Brain

Electroencephalography (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain through electrodes placed on the scalp. EEG can be used for various research applications, including studying brain function and activity, identifying neurological disorders, and investigating the effects of drugs or other interventions on brain activity. EEG is particularly useful for studying brain activity in real-time and identifying the timing and location of brain activity associated with specific cognitive processes or behaviors. It can also be used in clinical settings to diagnose and monitor neurological disorders such as epilepsy, sleep disorders, and traumatic brain injuries. Additionally, EEG can be used to investigate the effects of various interventions, such as cognitive training or neurofeedback, on brain activity and function.


What Causes the "Stoke" in Surfing?

For surfers, catching the perfect wave can induce a state of pure ecstasy known as the “stoke”. But what’s happening in the brain during this ultimate ride? Wearable Sensing created a custom dry EEG system that measures brainwaves during surfing. They partnered with Red Bull to use this technology on professional surfers to uncover the neurophysiological aspects of surfing. The dry EEG system is worn on the head like a swimming cap, and it allows for the measurement of brain activity in real-time during surfing. By studying the brainwaves of surfers during their best rides, researchers hope to understand what goes on in the brain during moments of flow and peak performance, and ultimately unlock the secrets to achieving that elusive state of “stoke”.

Predicting hypoxic hypoxia using machine learning and wearable sensors

In this study, wearable sensors and machine learning-based algorithms were used to predict hypoxia in-flight. The group used Wearable Sensing’s dry-EEG technology to collect sensor data from 85 participants during a two-phase study. Participants wore aviation flight masks, which regulated their oxygen intake while performing cognitive tests and simulated flying tasks. EEG data was collected and analyzed using principal component analysis and machine learning algorithms, including Naïve Bayes, decision tree, random forest, and neural network algorithms, to classify the data as normal or hypoxic. The results showed high sensitivity and specificity, indicating potential for developing a real-time, in-flight hypoxia detection system.

An experimental protocol for exploration of stress in an immersive VR scenario with EEG

This paper proposes a protocol for assessing stress using wearable sensing technology, including Electroencephalography (EEG), Electrocardiography (ECG), and the Perceived Stress Scale, in combination with a Virtual Reality phobia induction setting. Wearable Sensing’s dry EEG technology is used to measure brain activity and investigate functional brain connectivity associated with stress. The proposed protocol can be expanded with the incorporation of machine learning algorithms for automatic stress level classification.



All of Wearable Sensing's Dry EEG systems can be utilized for Brain Computer Interfaces

NeuSenW High-Density EEG

All of Wearable Sensing's Wet EEG systems can be utilized for Brain Computer Interfaces



Machine learning algorithms for cognitive state classification, such as mental workload, engagement, and fatigue

3rd Party Compatible Software

List of compatible software, including Neurofeedback, BCI, EEG Analysis, SDK's, and more


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Matthews, Robert; McDonald, Neil J; Anumula, Harini; Trejo, Leonard J

Novel Hybrid Sensors for Unobtrusive Recording of Human Biopotentials Conference

vol. 2006, AugCog International Conference Citeseer, 2006.

Abstract | Links | BibTeX


Wallerius, John; Trejo, Leonard J; Matthews, Robert; Rosipal, Roman; Caldwell, John A

Robust Feature Extraction and Classification of EEG Spectra for Real-time Classification of Cognitive State Technical Report


Abstract | Links | BibTeX

Matthews, Robert; McDonald, NJ; Fridman, I; Hervieux, P; Nielsen, T

The invisible electrode-zero prep time, ultra low capacitive sensing Journal Article

In: Foundations of Augmented Cognition, pp. 221, 2005.

Abstract | Links | BibTeX

McCormick, DT; Tien, NC; MacDonald, N; Matthews, R; Hibbs, A

Ultra-wide tuning range silicon MEMS capacitors on glass with Tera-ohm isolation and low parasitics Conference

The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS'05., vol. 1, IEEE 2005.

Abstract | Links | BibTeX

Matthews, Robert; McDonald, Neil J; Trejo, Leonard J

Psycho-Physiological Sensor Techniques: An Overview Journal Article

In: Foundations of augmented cognition, vol. 11, pp. 263–272, 2005.

Abstract | Links | BibTeX


Trejo, Leonard J; Wheeler, Kevin R; Jorgensen, Charles C; Rosipal, Roman; Clanton, Sam T; Matthews, Bryan; Hibbs, Andrew D; Matthews, Robert; Krupka, Michael

Multimodal neuroelectric interface development Journal Article

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 199–203, 2003.

Abstract | Links | BibTeX

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