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.

Applications

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.

Hardware

DSI Dry EEG

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

Software

QStates

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

Publications

123 entries « 12 of 13 »

2009

Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M

Physiological Sensor Suite Using Zero Preparation Hybrid Electrodes for Real Time Workload Classification Journal Article

In: The International Test and Evaluation Association, vol. 30, pp. 13–17, 2009.

Abstract | Links | BibTeX

Sellers, Eric W; Turner, Peter; Sarnacki, William A; McManus, Tobin; Vaughan, Theresa M; Matthews, Robert

A Novel Dry Electrode for Brain-Computer Interface Conference

International Conference on Human-Computer Interaction, Springer 2009.

Abstract | Links | BibTeX

2008

Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M

Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes Conference

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2008.

Abstract | Links | BibTeX

2007

Matthews, Robert; McDonald, Neil J; Hervieux, Paul; Turner, Peter J; Steindorf, Martin A

A Wearable Physiological Sensor Suite for Unobtrusive Monitoring of Physiological and Cognitive State Conference

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2007, ISSN: 1558-4615.

Abstract | Links | BibTeX

Matthews, Robert; McDonald, Neil J; Anumula, Harini; Woodward, Jamison; Turner, Peter J; Steindorf, Martin A; Chang, Kaichun; Pendleton, Joseph M

Novel Hybrid Bioelectrodes for Ambulatory Zero-Prep EEG Measurements Using Multi-channel Wireless EEG System Conference

International Conference on Foundations of Augmented Cognition, Springer 2007.

Abstract | Links | BibTeX

Trejo, Leonard J; McDonald, Neil J; Matthews, Robert; Allison, Brendan Z

Experimental design and testing of a multimodal cognitive overload classifier Journal Article

In: Foundations of Augmented Cognition, vol. 2007, pp. 13–22, 2007.

Abstract | Links | BibTeX

2006

Park, Chulsung; Chou, Pai H; Bai, Ying; Matthews, Robert; Hibbs, Andrew

An ultra-wearable, wireless, low power ECG monitoring system Conference

2006 IEEE biomedical circuits and systems conference, IEEE 2006.

Abstract | Links | BibTeX

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

2005

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

2005.

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

123 entries « 12 of 13 »

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