Valentin Goverdovsky

In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring

We introduce a novel in-ear sensor which satisfies key design requirements for wearable electroencephalography (EEG)-it is discreet, unobtrusive, and capable of capturing high-quality brain activity from the ear canal. Unlike our initial designs, which utilize custom earpieces and require a costly and time-consuming manufacturing process, we here introduce the generic earpieces to make ear-EEG suitable for immediate and widespread use. Our approach represents a departure from silicone earmoulds to provide a sensor based on a viscoelastic substrate and conductive cloth electrodes, both of which are shown to possess a number of desirable mechanical and electrical properties. Owing to its viscoelastic nature, such an earpiece exhibits good conformance to the shape of the ear canal, thus providing stable electrode-skin interface, while cloth electrodes require only saline solution to establish low impedance contact. The analysis highlights the distinguishing advantages compared with the current state-of-the-art in ear-EEG. We demonstrate that such a device can be readily used for the measurement of various EEG responses.

Co-Located Multimodal Sensing: A Next Generation Solution for Wearable Health

A novel physiological sensor which combines electrical and mechanical modalities is introduced. The electrical component behaves as a standard electrode and detects changes in bioelectrical potential, whereas the mechanical component comprises an electret condenser microphone with a thin and light diaphragm, making it sensitive to local mechanical activity but immune to global body movements. A key feature of the proposed sensor is that the microphone is positioned directly on top of the electrode component (co-location). In conjunction with co-located electromechanical sensing, the ability of the electrode to flex allows for motion to be detected at the same location where it corrupts the electrical physiological response. Thus, the output of the mechanical sensor can be used to reject motion-induced artifacts in physiological signals, offering improved recording quality in wearable health applications. We also show that the co-located electrical and mechanical modalities provide derived information beyond unimodal sensing, such as pulse arrival time and breathing, thus enhancing the utility of the proposed device and highlighting its potential as a diagnostic tool.

Hearables: Multimodal physiological in-ear sensing

Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.

Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV

It is generally accepted that the activities of the autonomic nervous system (ANS), which consists of the sympathetic (SNS) and parasympathetic nervous systems (PNS), are reflected in the low- (LF) and high-frequency (HF) bands in heart rate variability (HRV)—while, not without some controversy, the ratio of the powers in those frequency bands, the so called LF-HF ratio (LF/HF), has been used to quantify the degree of sympathovagal balance. Indeed, recent studies demonstrate that, in general: (i) sympathovagal balance cannot be accurately measured via the ratio of the LF- and HF- power bands; and (ii) the correspondence between the LF/HF ratio and the psychological and physiological state of a person is not unique. Since the standard LF/HF ratio provides only a single degree of freedom for the analysis of this 2D phenomenon, we propose a joint treatment of the LF and HF powers in HRV within a two-dimensional representation framework, thus providing the required degrees of freedom. By virtue of the proposed 2D representation, the restrictive assumption of the linear dependence between the activity of the autonomic nervous system (ANS) and the LF-HF frequency band powers is demonstrated to become unnecessary. The proposed analysis framework also opens up completely new possibilities for a more comprehensive and rigorous examination of HRV in relation to physical and mental states of an individual, and makes possible the categorization of different stress states based on HRV. In addition, based on instantaneous amplitudes of Hilbert-transformed LF- and HF-bands, a novel approach to estimate the markers of stress in HRV is proposed and is shown to improve the robustness to artifacts and irregularities, critical issues in real-world recordings. The proposed approach for resolving the ambiguities in the standard LF/HF-ratio analyses is verified over a number of real-world stress-invoking scenarios.

Smart Helmet: Wearable Multichannel ECG and EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.

Subspace denoising of EEG artefacts via multivariate EMD

The components obtained using the time-frequency algorithm empirical mode decomposition (EMD) enable unique advantages in the context of noise removal. In this paper, recent EMD-based de-noising methods are reviewed and similarities with a more conventional class of techniques - subspace denoising - are illustrated. Standard subspace approaches which are based on the factorisation of covariance matrices are unsuitable for nonstationary data. By comparison, EMD facilitates a signal representation which enables denoising using short spatio/temporal windows. It is highlighted how the EMD property of local orthogonality can be extended via multivariate operations, and a denoising scheme is proposed and compared with standard methods in electroencephalogram (EEG) artefact-removal using a novel multimodal sensor.

Smart helmet: Monitoring brain, cardiac and respiratory activity.

The timing of the assessment of the injuries following a road-traffic accident involving motorcyclists is absolutely crucial, particularly in the events with head trauma. Standard apparatus for monitoring cardiac activity is usually attached to the limbs or the torso, while the brain function is routinely measured with a separate unit connected to the head-mounted sensors. In stark contrast to these, we propose an integrated system which incorporates the two functionalities inside an ordinary motorcycle helmet. Multiple fabric electrodes were mounted inside the helmet at positions featuring good contact with the skin at different sections of the head. The experimental results demonstrate that the R-peaks (and therefore the heart rate) can be reliably extracted from potentials measured with electrodes on the mastoids and the lower jaw, while the electrodes on the forehead enable the observation of neural signals. We conclude that various vital sings and brain activity can be readily recorded from the inside of a helmet in a comfortable and inconspicuous way, requiring only a negligible setup effort.

In-Ear EEG Biometrics for Feasible and Readily Collectable Real-World Person Authentication

The use of electroencephalogram (EEG) as a biometrics modality has been investigated for about a decade; however, its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a “one-fits-all” viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis, and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.

Automatic Sleep Monitoring Using Ear-EEG

The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings; and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate substantial to almost perfect agreement, while for Scenario 2 the range of 0.65–0.80 indicates substantial agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.

Modular Software-Defined Radio Testbed for Rapid Prototyping of Localization Algorithms

A fully synchronized modular multichannel software-defined radio (SDR) testbed has been developed for the rapid prototyping and evaluation of array processing algorithms. Based on multiple universal software radio peripherals, this testbed is low cost, wideband, and highly reconfigurable. The testbed can be used to develop new techniques and algorithms in a variety of areas including, but not limited to, direction finding, source triangulation, and wireless sensor networks. A combination of hardware and software techniques is presented, which is shown to successfully remove the inherent phase and frequency uncertainties that exist between the individual SDR peripherals. The adequacy of the developed techniques is demonstrated through the application of the testbed to super-resolution direction finding algorithms, which rely on accurate phase synchronization.