New directions in EEG measurement: an investigation into the fidelity of electrical potential sensor signals

Low frequency noise performance is the key indicator in determining the signal to noise ratio of a capacitively coupled sensor when used to acquire electroencephalogram signals. For this reason, a prototype Electric Potential Sensor device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10 Hz or less. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and have diameters of 12 mm or 18 mm. In both cases, the sensors are housed in inert stainless steel machined housings with the electronics fabricated in surface mount components on a printed circuit board compatible with epoxy potting compounds. Potted sensors are designed to be immersed in alcohol for sterilization purposes. A comparative study was conducted with a commercial wet gel electrode system. These studies comprised measurements of both free running electroencephalogram and Event Related Potentials. Quality of the recorded electroencephalogram was assessed using three methods of inspection of raw signal, comparing signal to noise ratios, and Event Related Potentials noise analysis. A strictly comparable signal to noise ratio was observed and the overall conclusion from these comparative studies is that the noise performance of the new sensor is appropriate

Using Facial Gestures to Drive Narrative in VR

We developed an exploratory VR environment, where spatial features and narratives can be manipulated in real time by the facial and head gestures of the user. We are using the Faceteq prototype, exhibited in 2017, as the interactive interface. Faceteq consists of a wearable technology that can be adjusted on commercial HMDs for measuring facial expressions and biometric responses. Faceteq project was founded with the aim to provide a human-centred additional tool for affective human-computer interaction. The proposed demo will exhibit the hardware and the functionality of the demo in real time

FACETEQ interface demo for emotion expression in VR

© 2017 IEEE.Faceteq prototype v.05 is a wearable technology for measuring facial expressions and biometric responses for experimental studies in Virtual Reality. Developed by Emteq Ltd laboratory, Faceteq can enable new avenues for virtual reality research through combination of high performance patented dry sensor technologies, proprietary algorithms and real-time data acquisition and streaming. Emteq founded the Faceteq project with the aim to provide a human-centered additional tool for emotion expression, affective human-computer interaction and social virtual environments. The proposed demonstration will exhibit the hardware and its functionality by allowing attendees to experience three of the showcasing applications we developed this year

FACETEQ; A novel platform for measuring emotion in VR

FaceTeq prototype v.05 is a wearable technology for measuring facial expressions and biometric responses for experimental studies in Virtual Reality. Developed by Emteq Ltd laboratory, FaceTeq can enable new avenues for virtual reality research through combination of high performance patented dry sensor technologies, proprietary algorithms and real-time data acquisition and streaming. FaceTeq project was founded with the aim to provide a human-centred additional tool for emotion expression, affective human-computer interaction and social virtual environments. The proposed poster will exhibit the hardware and its functionality

A comparative study of electrical potential sensors and Ag/AgCl electrodes for characterising spontaneous and event related electroencephalagram signals

For exactly 90 years researchers have used electroencephalography (EEG) as a window into the activities of the brain. Even now its high temporal resolution coupled with relatively low cost compares favourably to other neuroimaging techniques such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). For the majority of this time the standard electrodes used for non-invasive monitoring of electrical activities of the brain have been Ag/AgCl metal electrodes. Although these electrodes provide a reliable method for recording EEG they suffer from noise, such as offset potential drift, and usability issues, for example, difficult skin preparation and cross-coupling of adjacent electrodes. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies of ~10 Hz or less. The EPS is a novel active ultrahigh impedance capacitively coupled sensor. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and scalable. A comprehensive study was undertaken to compare the results of neural signals recorded by the EPS with a standard commercial EEG system. These studies comprised measurements of both free running EEG and Event Related Potentials (ERPs). Results demonstrate that the EPS provides a promising alternative, with many added benefits compared to standard EEG sensors, including reduced setup time, elimination of sensor cross-coupling, lack of a ground electrode and distortion of electrical potentials encountered when using standard gel electrodes. Quantitatively, highly similar signals were observed between the EPS and EEG sensors for both free running and evoked brain activity with cross correlations of higher than 0.9 between the EPS and a standard benchmark EEG system. Future developments of EPS-based neuroimaging include the implementation of a whole head ultra-dense EPS array, and the mapping of distributions of scalp recorded electrical potentials remotely

Towards smart glasses for facial expression recognition using OMG and machine learning

Abstract This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes. The statistical analysis demonstrated that: (i) OCO sensors based on the principles of OMG can capture distinct variations in cheek and brow movements with a high degree of accuracy and specificity; (ii) Head movement does not have a significant impact on how well these facial expressions are detected. The collected data were also used to train a machine learning model to recognise the four facial expressions and when the face enters a neutral state. We evaluated this model in conditions intended to simulate real-world use, including variations in expression intensity, head movement and glasses position relative to the face. The model demonstrated an overall accuracy of 93% (0.90 f1-score)—evaluated using a leave-one-subject-out cross-validation technique