Papers

Quasar & Third-Party Publications

Select Quasar Publications

Abstract: Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session.
Abstract: Purpose: Neonatal seizures are a common neurologic diagnosis in neonatal intensive care units, occurring in approximately 14,000 newborns annually in the United States. Although the only reliable means of detecting and treating neonatal seizures is with an electroencephalography (EEG) recording, many neonates do not receive an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include (1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, (2) poor signal quality because of improper skin preparation and artifact, and (3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not previously been evaluated on neonates. Methods: Sequential and simultaneous recordings with wet and dry sensors were performed for 1 hour on 27 neonates from 35 to 42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined. Results: Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8), given the nonsuperimposed sensor positions and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically relevant EEG background and seizure patterns. There was no skin injury after 1 hour of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix. Conclusions: Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury
Abstract: This paper describes a project whose goal was to establish the feasibility of using unobtrusive cognitive assessment methodologies in order to optimize efficiency and expediency of training. QUASAR, EyeTracking, Inc. (ETI), and Safe Passage International (SPI), teamed to demonstrate correlation between EEG and eye-tracking based cognitive workload, performance assessment and subject expertise on XRay screening tasks. Results indicate significant correlation between cognitive workload metrics based on EEG and eye-tracking measurements recorded during a simulated baggage screening task and subject expertise and error rates in that same task. These results suggest that cognitive monitoring could be useful in improving training efficiency by enabling training paradigms that adapts to increasing expertise.
Abstract: This paper describes measurements made using an ECG system with QUASAR's capacitive bioelectrodes integrated into a pad system that is placed over a chair. QUASAR's capacitive bioelectrode has the property of measuring bioelectric potentials at a small separation from the body. This enables the measurement of ECG signals through fabric, without the removal of clothing or preparation of skin. The ECG was measured through the subject's clothing while the subject sat in the chair without any supporting action from the subject. The ECG pad system is an example of a high compliance system that places minimal requirements upon the subject and, consequently, can be used to generate a long-term record from ECG segments collected on a daily basis, providing valuable information on long-term trends in cardiac health.
Abstract: The Cognitive State Assessment Competition 2011 was organized by the U.S. Air Force Research Laboratory (AFRL) to compare the performance of real-time cognitive state classification software. This paper presents results for QUASAR’s data classification module, QStates, which is a software package for real-time (and off-line) analysis of physiologic data collected during cognitive-specific tasks. The classifier’s methodology can be generalized to any particular cognitive state; QStates identifies the most salient features extracted from EEG signals recorded during different cognitive states or loads.
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.
Abstract: QUASAR is working closely with the Aberdeen Test Center (ATC) 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 (DARPA) Augmented Cognition (AugCog) program and the U.S. Army’s Warfighter Physiological Status Monitor (WPSM) 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. QUASAR is developing robust gauges for classification of cognitive workload, engagement and fatigue, which simplify complex physiological data into one-dimensional parameters that can be used to identify a subject’s cognitive state during complex tasks 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 (EEG). The PSS is based on revolutionary noninvasive bioelectric sensor technologies pioneered by QUASAR. 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.
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.
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.
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.
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 O. 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.
Abstact: 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 volt- ages. 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 re- cording fidelity, noise characteristics, ease of use, and reliability of the hybrid sensors versus the conventional conductive electrodes in all contexts.
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
Abstract: Under the auspices of the Defense Advanced Research Projects Agency (DARPA), the Augmented Cognition (AugCog) program is striving to realize the unambiguous determination of the cognitive state via physiological measurements. The basis of this program 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, to infer the cognitive state it is possible to use psycho-physiological techniques, 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 program 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 that incorporates those sensors identified as being best suited to the determination of the cognitive state will be proposed.
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 non-invasive 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.
Abstract: Theoretical and experimental results of a design methodology and fabrication technology to realize ultrawide tuning range, electrostatic, silicon micromachined capacitors are presented. The varactors achieve a maximum tuning range of approximately 4000% and exhibit a linear tuning range of 1000% (C vs. V2). The devices are also designed and characterized with Tera-ohm isolation and sub 30 fF capacitive coupling between the driving actuator and tuning element. In addition, parasitic capacitances have been minimized to less than 22 fF at the tuning element terminals.
Abstract: We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

Select Third Party Publications

Abstract: Objectives To explore neuroplasticity in a longitudinal study of acute mild traumatic brain injury (mTBI). Methods We are using quantitative electroencephalography (qEEG) and magnetoencephalography (MEG) during the resting state and during cognitive brain stress to explore neuroplasticity in an ongoing acute mild traumatic brain injury research. Acute mTBI patients are recruited from the emergency department of Huntington Memorial Hospital in Pasadena, CA, and controls are non-head-trauma patients. Brain stress includes the N-back (0-back and 2-back) working memory test and Color-Word Interference Test (CWIT), administered using E-prime software. Data were collected at three time points: within 1 week of injury, 14 days, and 30 days after injury. Behavioral as well as MEG and qEEG analysis are performed to compare the two groups. Results Resting MEG detected low frequency activity in the mTBI group, consistent with previous publications. N-back, in particular during 2-back trials, and CWIT, in particular during incongruent trials, both show initial executive function impairment that improved on later visits. Time frequency analysis suggested corresponding compromised brain activity. Conclusions The EEG/MEG recordings during rest and brain stress are objective and sensitive to neuroplasticity in acute mTBI, and could be potential objective mTBI markers.
Abstract: Objective: This purpose of this study was to evaluate the usefulness of a prototype battery-powered dry electrode system (DES) EEG recording headset in Veteran patients by comparing it with standard EEG. Methods: Twenty-one Veterans had both a standard electrode system recording and DES recording in nine different patient states at the same encounter. Setup time, patient comfort, and subject preference were measured. Three experts performed technical quality rating of each EEG recording in a blinded fashion using the web-based EEGnet system. Power spectra were compared between DES and standard electrode system recordings. Results: The average time for DES setup was 5.7 minutes versus 21.1 minutes for standard electrode system. Subjects reported that the DES was more comfortable during setup. Most subjects (15 of 21) preferred the DES. On a five-point scale (1—best quality to 5—worst quality), the technical quality of the standard electrode system recordings was significantly better than for the DES recordings, at 1.25 versus 2.41 (P < 0.0001). But experts found that 87% of the DES EEG segments were of sufficient technical quality to be interpretable. Conclusions: This DES offers quick and easy setup and is well tolerated by subjects. Although the technical quality of DES recordings was less than standard EEG, most of the DES recordings were rated as interpretable by experts. Significance: This DES, if improved, could be useful for a telemedicine approach to outpatient routine EEG recording within the Veterans Administration or other health system.
Abstract: Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline. The wireless systems compared include Advanced Brain Monitoring's B-Alert X10, Emotiv Systems' EPOC and the 2009 version of QUASAR's Dry Sensor Interface 10–20. The design of each wireless system is discussed in relation to its impact on the system's usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.
Abstract: This study examines the difference in application times for routine electroencephalography (EEG) utilizing traditional electrodes and a “dry electrode” headset. The primary outcome measure was the time to interpretable EEG (TIE). A secondary outcome measure of recording quality and interpretability was obtained from EEG sample review by two blinded clinical neurophysiologists. With EEG samples obtained from 10 subjects, the average TIE for the “dry electrode” system was 139 s, and for the conventional recording 873 s (p < 0.001). The results support the hypothesis that such a “dry electrode” system can be applied with more than an 80% reduction in the TIE while still obtaining interpretable EEG.
Abstract: Recent advances in technology have provided a variety of hardware approaches for EEG collection, even with wireless applications. Because most neuroscientific research involves the relationship between some event and a neural response, maintaining proper temporal alignment in the measurement of these factors is crucial. Most devices use a separate internal clock for data logging, and traditional techniques involve multiple stimulus/recording devices connected via trigger cable for on-line synchronization. However, post-hoc analyses commonly require additional event information, typically gathered from 3rd party log files which often are not synchronized. Here, we discuss the timing and related characteristics of several contemporary EEG systems, including advanced wireless devices intended for mobile recording (Advanced Brain Monitoring, Emotiv, & Quasar) and standard “fixed laboratory” wired (Biosemi) applications. Variability in EEG-system-reported timing was small and relatively consistent for all systems using an external parallel/TTL-level input; the Emotiv Epoc, however, which natively uses only RS-232 serial for external input consistently erred up to +/-60 ms relative to true input time on each pulse. Clock timing drift varied widely across systems, ranging from negligible (0.00001%) to as much as 0.028%; in the latter case resulting in as much as 168ms error in stated time after a 10 min recording. To illustrate the practical impact of this error, we present results of simulations using both real human data, and virtual data using EEG phantom signal techniques, with varied levels of timing drift and jitter. For example, data from a 10-minute visual discrimination sample set show a robust P1/N1 event-related potential (ERP) response at electrode O2. Introduction of simulated timing drift of only 0.01% diminishes the P1 response to the level of background, and drastically decreases the N1 component by > 14%. Meanwhile, a drift matching that observed in some systems (0.03%) completely eliminates all early components. These results suggest that if unaccounted for, simple drift can substantially reduce epoched signal quality. Additional Monte-Carlo style simulation using known-quantity signals (e.g. square-wave) from a phantom source illustrates supplemental methods to quantify these effects for a particular system and identify minimum tolerance for specific applications. We discuss solutions for avoiding and resolving these potential problems, including algorithms for detection and compensation that can be built into standard pre-processing steps to ensure or even enhance the temporal fidelity of otherwise poor quality data.
Abstract: Advances in state-of-the-art dry electrode technology have led to the development of a novel dry electrode system for electroencephalography (QUASAR, Inc.; San Diego, California, USA). While basic systems-level testing and comparison of this dry electrode system to conventional wet electrode systems has proved to be very favorable, very limited data has been collected that demonstrates the ability of QUASAR’s dry electrode system to replicate results produced in more applied, dynamic testing environments that may be used for human factors applications. In this study, QUASAR’s dry electrode headset was used in combination with traditional wet electrodes to determine the ability of the dry electrode system to accurately differentiate between varying levels of cognitive workload. Results show that the accuracy in cognitive workload assessment obtained with wet electrodes is comparable to that obtained with the dry electrodes.
Abstract: Evolving military systems have the potential to inundate Soldiers with complex information and overwhelming tasks. Testers and evaluators need a way to measure the equipment’s effect on the Soldier and their performance. In 2004, ATC began work with Quantum Applied Science and Research (QUASAR) to develop an EEG headset (Figure 1) for use in a rugged T&E environment with sophisticated algorithms are then applied to the processed EEG data to provide measures of mental workload, engagement and fatigue. The first objective of this investigation was to validate the approach proposed herein for follow-on use in a study to determine the quality of the mental workload data obtained from the EEG headset as compared to data obtained from National Aeronautics and Space Association (NASA) Task Load Index (TLX). The second objective was to investigate the use of the EEG headset as a training quality evaluation instrument. Three subjects were chosen at random to participate in the study, where they were to complete 45 single column or 3-column addition questions. At the end of each trial, the subject was asked to complete the 6 question NASA-TLX questionnaire using ATC’s eQuestionnaire. A repeated measures analysis was used on the resulting data. Parameter tests were conducted across subject, trial, and workload condition for each of the five response variables (NASA-TLX, MNVPDF, Linear, Performance, and Time). Both the NASA-TLX and the EEG-based measures showed the ability to discriminate between the workload conditions. The EEG-based measures were better discriminators than the NASA-TLX. Learning effects were observed with the subjects getting a higher percentage of problems right over the trials and decreasing their completion time. EEG-based workload measures showed superior discriminatory power over the NASA-TLX. The NASA-TLX is hampered by biases injected from the subject’s internal rating system. The EEG-based measures removes the biases and applies everyone’s rating using the same scaling markers.
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.