Electroencephalography (EEG) is the measurement of electric potentials at the scalp due to currents flowing through scalp tissue. The strength and distribution of currents (and therefore potentials) reflects the intensity and position of activity in the underlying neural tissue. EEG signal is measured between two electrodes, the position of which determines the recorded brain area. Multiple electrodes are typically placed in standard arrangements that cover the entire scalp and allow investigators to observe the activity of the entire brain simultaneously.
EEG is typically recorded as a time-series of potential differences, which can be evaluated visually, or analyzed spectrally, or through the use of source localization methods. The principal spectral components of EEG are divided into the following signal bands: delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (above 12 Hz) and gamma (above 40 Hz). Many studies have related changes in various spectral components of EEG to specific cognitive functions and clinical conditions.
In clinical settings, continuous EEG recordings are used for monitoring sleep and anesthesia, and diagnosis of epilepsy, coma, brain death, and more recently ADHD. In research environments, EEG recordings are used in wide range of applications, including: neuroscience and cognitive psychology research into understanding brain function; Brain-Computer Interfaces to allow control of computers or machines from neural signals; neurofeedback where users are able to train their brains to generate specific activity patterns; neuromarketing where marketing companies seek to tap directly into brain signals to assess subjects’ engagement levels; neuroergonomics where researchers quantify mental workload under various strains; gaming where EEG signals are used to control computer games and toys; etc.EEG is typically recorded as a time-series of potential differences, which can be evaluated visually, or analyzed spectrally, or through the use of source localization methods. The principal spectral components of EEG are divided into the following signal bands: delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (above 12 Hz) and gamma (above 40 Hz). Many studies have related changes in various spectral components of EEG to specific cognitive functions and clinical conditions.
Conventional EEG systems require highly trained technicians to abrade the scalp and apply electrodes and conductive gels. The process is time consuming, irritating to users, and prone to noise and artifacts, thereby limiting the practical usability of EEG monitoring in applied environments. QUASAR’s dry electrode EEG sensors overcome this technical hurdle by eliminating the need for abrasive and conductive gels EEG and enabling rapid and minimally intrusive recordings in naturalistic environments.
This uniquely high fidelity yet practical EEG recording technology builds on dry electrode sensors developed by QUASAR under funding from DARPA, the Army, the Air Force, the NSF and the NIH, and integrated into headset designs that allow use by minimally trained personnel without assistance. These ultra-high impedance dry sensors record high quality EEG through hair without the need for skin preparation of any kind. (Figure 1A) QUASAR’s dry sensors rely on several technological advances for their performance, including patented circuit design, active cancellation of common mode electrical artifacts (such as triboelectric discharges or 60Hz mains) for reduction of artifacts and environmental noise, and shielding to reduce sensitivity to electrostatic noise. 1,2,3 Furthermore, the mechanical structure of the headset includes a nested design of spring-isolated pods that ensures electrode stability and motion isolation, allowing artifact-free recordings during ambulation. (Figure 1B)
Figure 1A: QUASAR’s through-hair sensor
Figure 1B: Wearable Sensing’s DSI-24
Practical sensing of biopotentials such as the electroencephalogram (EEG) 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 low impedance connection from the body to ground for cancellation of common-mode noise voltages. In this report we describe EEG results acquired using EEG hardware based upon dry contact electrode technology, and which uses a proprietary common-mode follower (CMF) which allows a dry electrode to be used for the ground. This article presents results auditory evoked potential measurements using Wearable Sensing’s DSI-24 system simultaneously with conventional (wet) EEG electrodes. The correlations between wet and dry electrodes (averaged over 3 subjects) were 93.6% and 95.7% for F3-P3 and F4-P4, respectively.
A total of 3 subjects were selected for testing of QUASAR’s EEG hardware, according to an IRB-approved protocol. The auditory ERP task used a tone generation routine (200 tones on PC speakers, average interval 2 seconds) to stimulate ERP signals. A trigger signal was output for each tone on a single line on the parallel port of the PC to the trigger inputs of EEG hardware.
Subjects wore Wearable Sensing’s DSI-24 dry electrode EEG headset, which includes integrated dry electrode biosensors positioned at approximate standard International 10/20 electrode locations. Wet electrode measurements were acquired using Ag/AgCl EEG electrode cups filled with Grass EC2 conductive EEG paste (Astro-Med, West Warwick, RI) and attached to sites on the subject’s scalp. The electrode sites were cleaned with alcohol to remove fats and then abraded with NuPrep (Weaver & Co., Aurora, CO). Wet electrode signals were acquired using a commercial passive wet electrode EEG amplifier that has 24-bit resolution on 16 channels of EEG and a single trigger input.The wet electrodes were positioned at F1, F5, F2, F6, P1, P2, P5, P6 electrode sites and the ground and reference electrodes were placed at the right earlobe and pinna, respectively.
The F3-P3 and F4-P4 vectors were digitally calculated from the the DSI-24 sensors. The equivalent signals for the wet electrodes were approximated by combining the wet electrode signals thus:
F3-P3 = (F1+F5)/2 – (P1+P5)/2 and F4-P4 = (F2+F6)/2 – (P2+P6)/2
Wet and Dry F3-P3 & F4-P4 signals were digitally filtered using Infinite Impulse Response (IIR) notch filters, and then bandpass filtered in a 1-40Hz bandwidth (-3dB). ERP epochs were obtained by taking an interval [-0.5s, +0.5s] around each trigger. Epochs in which the filtered signal magnitude exceeded 50 microV were rejected. The sample correlation coefficient was then calculated between the average dry electrode ERP and average wet electrode ERP signals.
The results for all three subjects are presented in the Figures to the right, which plot the average ERP signals in the interval from 500ms preceding the trigger to 500ms following a trigger. Correlations between wet and dry electrodes (averaged across 3 subjects) for the intervals shown are 93.6% and 95.7% for F3-P3 and F4-P4, respectively.
In addition, average signal to noise ratios (SNRs) for ERP amplitude over pre-trigger noise RMS voltage across 3 subjects and vectors were 11.8 +/- 5.5 and 12.6 +/-2.2 for dry and wet recordings respectively, indicating equivalent SNR.
Simultaneous measurements of ERP signals using dry electrode and wet electrodes excellent conservation of signal morphology between signals obtained from wet and dry electrodes; both in the pre-trigger “noise” segment, and in the N100-P200 ERP component. This is evident both in a visual inspection of the traces presented in the illustrative Figures, and also by the fact that the correlation values exceed 90% for both anterior-posterior ERP signals and that the SNRs for both electrode technologies are equivalent.
In order to be adopted and used in an applied environment, the monitoring system should be easy to use, comfortable and not interfere with task performance. Accordingly, QUASAR’s headset utilizes a patented combination of spring-loaded arms and sensor mounts to allow it to be put rapidly on the head in the manner of a baseball cap, and controlled expansion joints automatically position 21 sensors simultaneously according the International 10/20 System over a wide range of head sizes. 4 During testing of the headset, the time to don the headset and begin EEG measurements was less than 5 minutes.
Importantly, these systems are also very comfortable. During a series of 4 or 8-hours long recording experiments, QUASAR surveyed the subjects on the comfort of the dry sensor EEG headset designed for seated environments. Subjects reported comfort on a category rating scale from 1 – 10, where 1 relates to “unnoticeable”, 5 “noticeable but comfortable”, and 10 “intolerable.” Figure 3 presents a summary of the survey data divided into 8 specific categories. Results indicate that on all categories the subjects reported high tolerance and comfort, and even after 8 hours of wear, subjects reported the headset was comfortable on all categorical measures. (subject drop-out is due to experiment end, not discomfort)
Figure 3. Comfort survey results for subjects wearing QUASAR’s EEG headset or helmet for up to 8 hours. Y-axis is average of subjective scale where 1 was considered “unnoticeable and 5 “comfortable but noticeable”, and 10 painful. (#) is number of subjects in time bin.
In summary, QUASAR’s dry sensor EEG technology is thus ideally suited for use in applications real-world training environments, both for its high fidelity, artifact-free signal quality, and for its practical, comfortable wireless headset that does not interfere with task performance. This technology is uniquely ready for practical use in training environments.
Wearable Sensing has licensed QUASAR’s technology for commercializing its applications.