Research

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

Learn More

NeuSenW High-Density EEG

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

Learn More

Software

QStates

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

Learn More

3rd Party Compatible Software

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

Learn More

Publications

149 entries « ‹ 15 of 15 › »

2007

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

@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}

}

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

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

@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}

}

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.

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

@article{matthews2005invisible,

title = {The invisible electrode-zero prep time, ultra low capacitive sensing},

author = {Robert Matthews and NJ McDonald and I Fridman and P Hervieux and T Nielsen},

url = {https://books.google.com/books?hl=en&lr=&id=sWO1DwAAQBAJ&oi=fnd&pg=PA221&dq=invisible&ots=ZU2692_xdh&sig=ig5dxx8kgdfm3gXtgD12pMZnv-o#v=onepage&q=invisible&f=false},

year  = {2005},

date = {2005-01-01},

journal = {Foundations of Augmented Cognition},

pages = {221},

publisher = {CRC Press},

abstract = {The principle technical difficulty in measuring bioelectric signals from the body, such as electroencephalogram 

(EEG) and electrocardiogram (ECG), lies in establishing good, stable electrical contact to the skin. Traditionally, 

measurements of human bioelectric activity use resistive contact electrodes, the most widely used of which are 

‘paste-on’ (or wet) electrodes. However, the use of wet electrodes is a highly invasive process as some preparation 

of the skin is necessary in order for the electrode either to adhere to the skin for any length of time or to make 

adequate electrical contact to the skin. This is uncomfortable for the subject and can lead to considerable irritation of 

the skin over time, an issue of particular concern in measurements of EEG signals, which typically require an array 

of electrodes positioned about the head. 

Despite over 40 years of investigation, including the development of several alternative electrode technologies, no 

reliable method for making electrical contact to the skin that does not require some modification of its outer layer 

has been developed. For example, Ag-AgCl dry electrodes, NASICON ceramic electrodes, and saline solution 

electrodes do not require any skin preparation, but for each electrode the subject experiences skin irritation over 

extended periods, and there are various issues that cause the performance of the sensors to degrade over time. 

Alternatively, insulated electrodes that use capacitive coupling to measure the potential changes on the skin have in 

the past, for noise considerations, used exotic materials to generate a high capacitive coupling (~1 nF) to the skin. 

The intrinsic noise of insulated electrodes is adequate for bioelectric measurements, but these high capacitance 

sensors also exhibit long-term compatibility issues with the skin and are sensitive to motion artifact signals due to 

the electrode’s high sensitivity to relative motion between the skin and the electrode itself. 

As a result of advances in semiconductor processing techniques and through the use of innovative circuit designs, 

QUASAR has developed a new class of insulated bioelectrodes (IBEs) that can measure the electric potential at a 

point in free space. This has made it possible to make measurements of human bioelectric signals without a resistive 

connection and with modest capacitive coupling to the source of interest. These electrodes are genuinely noninvasive in that they require no skin preparation, have no long-term compatibility issues with the skin, and can 

measure human bioelectric activity at the microvolt level through clothing while remaining largely immune to 

motion artifact signals. 

This paper will present measurements of bioelectric activity made using QUASAR’s IBEs, and corresponding data 

measured using conventional wet electrodes will also be presented for comparison. The presentation will include 

through-clothing measurements of bioelectric signals, the rejection of motion artifact signals, non-invasive (i.e. no 

skin preparation) EEG measurements of alpha-rhythm signals, and the noise levels observed using both types of 

sensors. 

In a series of tests conducted on unprepared skin, it was observed that both the IBEs and conventional wet electrodes 

had similar noise levels. This noise level was higher than the expected noise level, which had been predicted based 

upon the intrinsic noise characteristics of the sensors. The fact that both sensors suffered from this higher noise level 

suggested a common mechanism, which was later identified as skin noise. 

It has been reported in the literature that one of the fundamental noise sources for any bioelectric measurement made 

on unprepared skin is epidermal artifact noise. This noise is due to potentials developed in the skin itself that are 

indistinguishable from the bioelectric signal of interest. There exist techniques that can reduce this noise level by as 

much as a factor of 5, but they involve modification of the skin’s outer layer either by abrasion or chemical absorption of conducting fluid. These methods are not comfortable for the subject and may be difficult to perform on 

subjects with especially sensitive skin, such as neonates, burn victims, or the elderly. 

In addition to QUASAR’s IBE sensors, this paper will also discuss a new free-space electrode that is designed to be 

insensitive to epidermal artifact noise, and thus is capable of bioelectric measurements at the microvolt level in the 

absence of any skin preparation. The new device exhibits significantly less capacitive coupling to the source of 

interest than the current generation of QUASAR IBEs, without the increase in intrinsic sensor noise that would 

accompany a reduction in electrode capacitance. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

The principle technical difficulty in measuring bioelectric signals from the body, such as electroencephalogram
(EEG) and electrocardiogram (ECG), lies in establishing good, stable electrical contact to the skin. Traditionally,
measurements of human bioelectric activity use resistive contact electrodes, the most widely used of which are
‘paste-on’ (or wet) electrodes. However, the use of wet electrodes is a highly invasive process as some preparation
of the skin is necessary in order for the electrode either to adhere to the skin for any length of time or to make
adequate electrical contact to the skin. This is uncomfortable for the subject and can lead to considerable irritation of
the skin over time, an issue of particular concern in measurements of EEG signals, which typically require an array
of electrodes positioned about the head.
Despite over 40 years of investigation, including the development of several alternative electrode technologies, no
reliable method for making electrical contact to the skin that does not require some modification of its outer layer
has been developed. For example, Ag-AgCl dry electrodes, NASICON ceramic electrodes, and saline solution
electrodes do not require any skin preparation, but for each electrode the subject experiences skin irritation over
extended periods, and there are various issues that cause the performance of the sensors to degrade over time.
Alternatively, insulated electrodes that use capacitive coupling to measure the potential changes on the skin have in
the past, for noise considerations, used exotic materials to generate a high capacitive coupling (~1 nF) to the skin.
The intrinsic noise of insulated electrodes is adequate for bioelectric measurements, but these high capacitance
sensors also exhibit long-term compatibility issues with the skin and are sensitive to motion artifact signals due to
the electrode’s high sensitivity to relative motion between the skin and the electrode itself.
As a result of advances in semiconductor processing techniques and through the use of innovative circuit designs,
QUASAR has developed a new class of insulated bioelectrodes (IBEs) that can measure the electric potential at a
point in free space. This has made it possible to make measurements of human bioelectric signals without a resistive
connection and with modest capacitive coupling to the source of interest. These electrodes are genuinely noninvasive in that they require no skin preparation, have no long-term compatibility issues with the skin, and can
measure human bioelectric activity at the microvolt level through clothing while remaining largely immune to
motion artifact signals.
This paper will present measurements of bioelectric activity made using QUASAR’s IBEs, and corresponding data
measured using conventional wet electrodes will also be presented for comparison. The presentation will include
through-clothing measurements of bioelectric signals, the rejection of motion artifact signals, non-invasive (i.e. no
skin preparation) EEG measurements of alpha-rhythm signals, and the noise levels observed using both types of
sensors.
In a series of tests conducted on unprepared skin, it was observed that both the IBEs and conventional wet electrodes
had similar noise levels. This noise level was higher than the expected noise level, which had been predicted based
upon the intrinsic noise characteristics of the sensors. The fact that both sensors suffered from this higher noise level
suggested a common mechanism, which was later identified as skin noise.
It has been reported in the literature that one of the fundamental noise sources for any bioelectric measurement made
on unprepared skin is epidermal artifact noise. This noise is due to potentials developed in the skin itself that are
indistinguishable from the bioelectric signal of interest. There exist techniques that can reduce this noise level by as
much as a factor of 5, but they involve modification of the skin’s outer layer either by abrasion or chemical absorption of conducting fluid. These methods are not comfortable for the subject and may be difficult to perform on
subjects with especially sensitive skin, such as neonates, burn victims, or the elderly.
In addition to QUASAR’s IBE sensors, this paper will also discuss a new free-space electrode that is designed to be
insensitive to epidermal artifact noise, and thus is capable of bioelectric measurements at the microvolt level in the
absence of any skin preparation. The new device exhibits significantly less capacitive coupling to the source of
interest than the current generation of QUASAR IBEs, without the increase in intrinsic sensor noise that would
accompany a reduction in electrode capacitance.

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

@article{matthews2005psycho,

title = {Psycho-Physiological Sensor Techniques: An Overview},

author = {Robert Matthews and Neil J McDonald and Leonard J Trejo},

url = {https://www.researchgate.net/profile/Leonard-Trejo/publication/266178283_Psycho-Physiological_Sensor_Techniques_An_Overview/links/5535a1650cf268fd0015e0cc/Psycho-Physiological-Sensor-Techniques-An-Overview.pdf},

year  = {2005},

date = {2005-01-01},

journal = {Foundations of augmented cognition},

volume = {11},

pages = {263--272},

publisher = {Erlbaum Mahwah, NJ},

abstract = {Under the auspices of the Defense Advanced Research Projects Agency (DARPA), the Augmented Cognition

(AugCog) programme is striving to realize the unambiguous determination of the cognitive state via physiological

measurements. The basis of this programme is the assumption that as computing power continues to increase, the

exchange of information between computers and their human users is fundamentally limited by human information

processing capabilities, particularly when the users are fatigued or placed under stressful conditions such as those

present in warfare command environments. Knowledge of the cognitive state will enable the development of a

human-computer interface that adapts to optimize the processing of information by the user.

At present there is no direct measure of a subject’s cognitive state. However, it is possible to use psycho-physiological

techniques to infer the cognitive state, in which changes in physiological signals that are affected by the cognitive

state (e.g. electroencephalographic signals, variations in the heart rate or blood flow in the brain) are measured using

a suite of bioelectric and biophysical sensors, and then processed using sophisticated algorithms based upon

theoretical descriptions of the relationship between the cognitive state and the relevant physiological signals.

This paper will provide an overview of current psycho-physiological related research, quoting recent results from

groups working in the AugCog programme and elsewhere. The latest advancements in sensor technologies will be

reviewed in an effort to identify those physiological sensors that are most useful in determining the cognitive state,

with particular attention paid to the quality of information provided by the sensor and design elements such as the

degree of comfort for the human test subject, sensor power requirements, ease of use, and cost. The compatibility of

each sensor type with other technologies will also be assessed and a psycho-physiological integrated sensor suite,

incorporating those sensors identified as being best suited to the determination of the cognitive state, will be proposed.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Under the auspices of the Defense Advanced Research Projects Agency (DARPA), the Augmented Cognition
(AugCog) programme is striving to realize the unambiguous determination of the cognitive state via physiological
measurements. The basis of this programme is the assumption that as computing power continues to increase, the
exchange of information between computers and their human users is fundamentally limited by human information
processing capabilities, particularly when the users are fatigued or placed under stressful conditions such as those
present in warfare command environments. Knowledge of the cognitive state will enable the development of a
human-computer interface that adapts to optimize the processing of information by the user.
At present there is no direct measure of a subject’s cognitive state. However, it is possible to use psycho-physiological
techniques to infer the cognitive state, in which changes in physiological signals that are affected by the cognitive
state (e.g. electroencephalographic signals, variations in the heart rate or blood flow in the brain) are measured using
a suite of bioelectric and biophysical sensors, and then processed using sophisticated algorithms based upon
theoretical descriptions of the relationship between the cognitive state and the relevant physiological signals.
This paper will provide an overview of current psycho-physiological related research, quoting recent results from
groups working in the AugCog programme and elsewhere. The latest advancements in sensor technologies will be
reviewed in an effort to identify those physiological sensors that are most useful in determining the cognitive state,
with particular attention paid to the quality of information provided by the sensor and design elements such as the
degree of comfort for the human test subject, sensor power requirements, ease of use, and cost. The compatibility of
each sensor type with other technologies will also be assessed and a psycho-physiological integrated sensor suite,
incorporating those sensors identified as being best suited to the determination of the cognitive state, will be proposed.

2003

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

149 entries « ‹ 15 of 15 › »

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Research

Discover the Why of the Brain

Applications

What Causes the "Stoke" in Surfing?

Predicting hypoxic hypoxia using machine learning and wearable sensors

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

Hardware

DSI Dry EEG

NeuSenW High-Density EEG

Software

QStates

3rd Party Compatible Software

Publications

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2006

2005

2003

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