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.
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”.
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.
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.
Estepp, Justin R; Christensen, James C; Monnin, Jason W; Davis, Iris M; Wilson, Glenn F
Validation of a Dry Electrode System for EEG Conference
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 53, no. 18, SAGE Publications Sage CA: Los Angeles, CA 2009.
@conference{estepp2009validation,
title = {Validation of a Dry Electrode System for EEG},
author = {Justin R Estepp and James C Christensen and Jason W Monnin and Iris M Davis and Glenn F Wilson},
doi = {https://doi.org/10.1177/154193120905301802},
year = {2009},
date = {2009-10-01},
booktitle = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
volume = {53},
number = {18},
pages = {1171--1175},
organization = {SAGE Publications Sage CA: Los Angeles, CA},
abstract = {Electroencephalography (EEG) has been used for over 80 years to monitor brain activity. The basic technology of using electrodes placed on the scalp with conductive gel or paste (“wet electrodes”) has not fundamentally changed in that time. An electrode system that does not require conductive gel and skin preparation represents a major advancement in this technology and could significantly increase the utility of such a system for many human factors applications. QUASAR, Inc. (San Diego, CA) has developed a prototype dry electrode system for EEG that may well deliver on the promises of dry electrode technology; before any such system could gain widespread acceptance, it is essential to directly compare their system with conventional wet electrodes. An independent validation of dry vs. wet electrodes was conducted; in general, the results confirm that the data collected by the new system is comparable to conventional wet technology.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@article{matthews2009physiological,
title = {Physiological Sensor Suite Using Zero Preparation Hybrid Electrodes for Real Time Workload Classification},
author = {R Matthews and PJ Turner and NJ McDonald and K Ermolaev and T Mc Manus and RA Shelby and M Steindorf},
url = {http://www.quasarusa.com/pdf/Matthews_ITEA_2009_Physiological%20Sensor%20Suite%20Using%20Zero%20Preparation%20Hybrid%20Electrodes%20for%20Real%20Time%20Workload%20Classification.pdf},
year = {2009},
date = {2009-01-01},
journal = {The International Test and Evaluation Association},
volume = {30},
pages = {13--17},
abstract = {Quantum Applied Science and Research is working closely with the Aberdeen Test Center to develop an integrated system to monitor warfighter physiology. This need has been recognized by two recent major programs: the Defense Advanced Research Projects Agency’s Augmented Cognition program and the U.S. Army’s Warfighter Physiological Status Monitor program. However, these programs were limited by inadequate development of fully deployable noninvasive sensors and in the number of physiological variables they could simultaneously measure. Warfighters need to rapidly perceive, comprehend, and translate combat information into action. To aid them, robust gauges have been developed for classification of cognitive workload, engagement, and fatigue, which simplify complex physiological data into onedimensional parameters that can be used to identify a subject’s cognitive state during the varied tasks carried out in a training environment. This article describes the two main hardware modules that form part of an integrated Physiological Sensor Suite (PSS): a Physiological Status Monitor (PSM) and a module for the measurement of electroencephalograms (EEGs). The PSS is based on revolutionary noninvasive bioelectric sensor technologies. No modification of the skin’s outer layer is required for the operation of this sensor technology, unlike conventional electrode technology that requires the use of conductive pastes or gels, often with abrasive skin preparation of the electrode site. The PSS was designed to be wearable and unobtrusive, with an emphasis on the capability of long-term monitoring of physiological signals. These factors are of considerable importance in operational settings where high end-user compliance is required. The PSM is a simple belt that is worn around the chest. The EEG system has already been incorporated into a soldier’s Kevlar helmet and tested successfully during combat training. Data are acquired using a miniature, ultralowpower, microprocessor-controlled multichannel data acquisition (DAQ) unit that transmits data wirelessly to a base station/data logger worn by the subject. The DAQ unit is worn on the body close to the measurement point, reducing the amount of cable clutter and minimizing the impact on subject mobility without introducing motion artifacts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
@conference{sellers2009novel,
title = {A Novel Dry Electrode for Brain-Computer Interface},
author = {Eric W Sellers and Peter Turner and William A Sarnacki and Tobin McManus and Theresa M Vaughan and Robert Matthews},
url = {https://link.springer.com/chapter/10.1007/978-3-642-02577-8_68},
year = {2009},
date = {2009-01-01},
booktitle = {International Conference on Human-Computer Interaction},
pages = {623--631},
organization = {Springer},
abstract = {A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based brain-computer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from currently available BCI technology. Independent BCI use requires easily acquired, good-quality electroencephalographic (EEG) signals maintained over long periods in less-than-ideal electrical environments. Conventional, wet-sensor, electrodes require careful application. Faulty or inadequate preparation, noisy environments, or gel evaporation can result in poor signal quality. Poor signal quality produces poor user performance, system downtime, and user and caregiver frustration. This study demonstrates that a hybrid dry electrode sensor array (HESA) performs as well as traditional wet electrodes and may help propel BCI technology to a widely accepted alternative mode of communication.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@conference{matthews2008real,
title = {Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes},
author = {R Matthews and PJ Turner and NJ McDonald and K Ermolaev and T Mc Manus and RA Shelby and M Steindorf},
doi = {10.1109/IEMBS.2008.4650550},
year = {2008},
date = {2008-10-14},
booktitle = {2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
pages = {5871--5875},
organization = {IEEE},
abstract = {This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Matthews, Robert; McDonald, Neil J; Hervieux, Paul; Turner, Peter J; Steindorf, Martin A
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2007, ISSN: 1558-4615.
@conference{matthews2007wearable,
title = {A Wearable Physiological Sensor Suite for Unobtrusive Monitoring of Physiological and Cognitive State},
author = {Robert Matthews and Neil J McDonald and Paul Hervieux and Peter J Turner and Martin A Steindorf},
doi = {10.1109/IEMBS.2007.4353532},
issn = {1558-4615},
year = {2007},
date = {2007-10-22},
booktitle = {2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
pages = {5276--5281},
organization = {IEEE},
abstract = {This paper describes an integrated Physiological Sensor Suite (PSS) based upon QUASAR's innovative noninvasive bioelectric sensor technologies that will provide, for the first time, a fully integrated, noninvasive methodology for physiological sensing. The PSS currently under development at QUASAR is a state-of-the-art multimodal array of that, along with an ultra-low power personal area wireless network, form a comprehensive body-worn system for real-time monitoring of subject physiology and cognitive status. Applications of the PSS extend from monitoring of military personnel to long-term monitoring of patients diagnosed with cardiac or neurological conditions. Results for side-by-side comparisons between QUASAR's biosensor technology and conventional wet electrodes are presented. The signal fidelity for bioelectric measurements using QUASAR's biosensors is comparable to that for wet electrodes.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Matthews, Robert; McDonald, Neil J; Anumula, Harini; Woodward, Jamison; Turner, Peter J; Steindorf, Martin A; Chang, Kaichun; Pendleton, Joseph M
International Conference on Foundations of Augmented Cognition, Springer 2007.
@conference{matthews2007novel,
title = {Novel Hybrid Bioelectrodes for Ambulatory Zero-Prep EEG Measurements Using Multi-channel Wireless EEG System},
author = {Robert Matthews and Neil J McDonald and Harini Anumula and Jamison Woodward and Peter J Turner and Martin A Steindorf and Kaichun Chang and Joseph M Pendleton},
url = {https://link.springer.com/chapter/10.1007/978-3-540-73216-7_16},
year = {2007},
date = {2007-01-01},
booktitle = {International Conference on Foundations of Augmented Cognition},
pages = {137--146},
organization = {Springer},
abstract = {This paper describes a wireless multi-channel system for zero-prep electroencephalogram (EEG) measurements in operational settings. The EEG sensors are based upon a novel hybrid (capacitive/resistive) bioelectrode technology that requires no modification to the skin’s outer layer. High impedance techniques developed for QUASAR’s capacitive electrocardiogram (ECG) sensors minimize the sensor’s susceptibility to common-mode (CM) interference, and permit EEG measurements with electrode-subject impedances as large as 107 Ω. Results for a side-by-side comparison between the hybrid sensors and conventional wet electrodes for EEG measurements are presented. A high level of correlation between the two electrode technologies (>99 subjects seated) was observed. The electronics package for the EEG system is based upon a miniature, ultra-low power microprocessor-controlled data acquisition system and a miniaturized wireless transceiver that can operate in excess of 72 hours from two AAA batteries.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@article{trejo2007experimental,
title = {Experimental design and testing of a multimodal cognitive overload classifier},
author = {Leonard J Trejo and Neil J McDonald and Robert Matthews and Brendan Z Allison},
url = {https://www.researchgate.net/profile/Leonard-Trejo/publication/266524682_Experimental_Design_and_Testing_of_a_Multimodal_Cognitive_Overload_Classifier/links/5535a1650cf20ea35f10ddf0/Experimental-Design-and-Testing-of-a-Multimodal-Cognitive-Overload-Classifier.pdf},
year = {2007},
date = {2007-01-01},
journal = {Foundations of Augmented Cognition},
volume = {2007},
pages = {13--22},
publisher = {Strategic Analysis, Inc Arlington, VA},
abstract = {We report the results of an experiment designed to construct and test a robust multimodal system for automatic classification of cognitive overload. With the assistance of The Scripps Research Institute, twenty-two experienced gamers performed a first-person shooter combat simulation while multiple biosignals and performance were recorded. Signals included EEG, EOG, EMG, ECG, accuracy, and reaction time measures. A kernel partial least squares or KPLS classifier was trained to distinguish subtle differences in EEG spectra within subjects as they pertained to passive viewing, low-difficulty, and high-difficulty simulations. The KPLS classifier was supported and enhanced by algorithms for preprocessing and normalization of EEG and other biosignals. Results indicated that for some subjects, robust classifiers discriminated passive viewing from active performance with accuracies in the range of 99% to 100% and that such models were stable over two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. In addition, for some subjects, classifiers discriminated high-difficulty simulations from low-difficulty and passive simulations with stable accuracies of 80% or better across two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. We also tested the effect of alcohol intoxication on simulation performance and classifier accuracy. Performance was slightly altered by drinking alcohol to a blood alcohol level of 0.06%, producing more aggressive behavior than with a placebo across subjects. The KPLS classifiers showed remarkable resilience to alcohol effects, considerably less than the effects of day of testing. Not all subjects had such impressive results. To address the lack of generality across subjects, we are currently performing a modified study design that includes provisions for three calibration tasks. These calibration tasks are intended to serve as brief, simple tasks that a user could easily perform at any time as needed to
recalibrate algorithms that relate physiology to cognitive state. The tasks include a) eyes-open and eyes-closed for general EEG calibration, b) a mental arithmetic task (divide by twos or sevens) for cognitive load classification, and c) passive viewing of the combat simulation task, for calibration of engagement/disengagement of mental resources. We aim to use the data from the calibration tasks to adapt classification algorithms for variations over time and for inclusion of multiple sensor and data modalities, such as electrocardiographic, electrooculographic, and electromyographic sensor data},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
@conference{park2006ultra,
title = {An ultra-wearable, wireless, low power ECG monitoring system},
author = {Chulsung Park and Pai H Chou and Ying Bai and Robert Matthews and Andrew Hibbs},
doi = {10.1109/BIOCAS.2006.4600353},
year = {2006},
date = {2006-11-29},
booktitle = {2006 IEEE biomedical circuits and systems conference},
pages = {241--244},
organization = {IEEE},
abstract = {Wearable electrocardiograph (ECG) monitoring systems today use electrodes that require skin preparation in advance, and require pastes or gels to make electrical contact to the skin. Moreover, they are not suitable for subjects at high levels of activity due to high noise spikes that can appear in the data. To address these problems, a new class of miniature, ultra low noise, capacitive sensor that does not require direct contact to the skin, and has comparable performance to gold standard ECG electrodes, has been developed. This paper presents a description and evaluation of a wireless version of a system based on these innovative ECG sensors. We use a wearable and ultra low power wireless sensor node called Eco. Experimental results show that the wireless interface will add minimal size and weight to the system while providing reliable, untethered operation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@conference{matthews2006novel,
title = {Novel Hybrid Sensors for Unobtrusive Recording of Human Biopotentials},
author = {Robert Matthews and Neil J McDonald and Harini Anumula and Leonard J Trejo},
url = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.452.1407&rep=rep1&type=pdf},
year = {2006},
date = {2006-01-01},
journal = {Foundations of Augmented Cognition},
volume = {2006},
pages = {91--101},
publisher = {Citeseer},
organization = {AugCog International Conference},
abstract = {Practical sensing of biopotentials such as EEG or ECG in operational settings has been severely limited by the need
for skin preparation and conductive electrolytes at the skin-sensor interface. Another seldom-noted problem has
been the need for a conductive connection from the body to ground for cancellation of common-mode noise voltages. At QUASAR, we have developed a novel hybrid (capacitive/conductive) sensor that requires no skin preparation or electrolytes. In addition we have developed a special common-mode follower that allows a dry electrode to
be used for the ground. The electronics for the sensors and common-mode follower have low power requirements
and are miniaturized to fit within a compact sensor case. We are extending our tests of the hybrid sensor in three
human-machine interaction contexts: 1) a real-time system of multimodal physiological gauges for improved human-automation reliability, 2) EEG-based cognitive-overload detection in an urban combat simulation, and 3) a
brain-computer interface for EEG-based communication in the severely disabled. In all three contexts we compared
the QUASAR hybrid sensors with traditional conductive electrodes for EEG or ECG recordings. We discuss the recording fidelity, noise characteristics, ease of use, and reliability of the hybrid sensors versus the conventional conductive electrodes in all contexts. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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.
@techreport{wallerius2005robust,
title = {Robust Feature Extraction and Classification of EEG Spectra for Real-time Classification of Cognitive State},
author = {John Wallerius and Leonard J Trejo and Robert Matthews and Roman Rosipal and John A Caldwell},
url = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.7141&rep=rep1&type=pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of 11th international conference on human computer interaction},
pages = {1--10},
organization = {Citeseer},
abstract = {We developed an algorithm to extract and combine EEG spectral features, which effectively classifies cognitive
states and is robust in the presence of sensor noise. The algorithm uses a partial-least squares (PLS) algorithm to
decompose multi-sensor EEG spectra into a small set of components. These components are chosen such that they
are linearly orthogonal to each other and maximize the covariance between the EEG input variables and discrete
output variables, such as different cognitive states. A second stage of the algorithm uses robust cross-validation
methods to select the optimal number of components for classification. The algorithm can process practically
unlimited input channels and spectral resolutions. No a priori information about the spatial or spectral distributions
of the sources is required. A final stage of the algorithm uses robust cross-validation methods to reduce the set of
electrodes to the minimum set that does not sacrifice classification accuracy. We tested the algorithm with simulated
EEG data in which mental fatigue was represented by increases frontal theta and occipital alpha band power. We
synthesized EEG from bilateral pairs of frontal theta sources and occipital alpha sources generated by second-order
autoregressive processes. We then excited the sources with white noise and mixed the source signals into a 19-
channel sensor array (10-20 system) with the three-sphere head model of the BESA Dipole Simulator. We generated
synthetic EEG for 60 2-second long epochs. Separate EEG series represented the alert and fatigued states, between
which alpha and theta amplitudes differed on average by a factor of two. We then corrupted the data with broadband
white noise to yield signal-to-noise ratios (SNR) between 10 dB and -15 dB. We used half of the segments for
training and cross-validation of the classifier and the other half for testing. Over this range of SNRs, classifier
performance degraded smoothly, with test proportions correct (TPC) of 94%, 95%, 96%, 97%, 84%, and 53% for
SNRs of 10 dB, 5 dB, 0 dB, -5 dB, -10 dB, and -15 dB, respectively. We will discuss the practical implications of
this algorithm for real-time state classification and an off-line application to EEG data taken from pilots who
performed cognitive and flight tests over a 37-hour period of extended wakefulness.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
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