No abstract

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis.

The use of 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.• Recording scalp-EEG with multiple electrodes is timeconsuming to set-up and cumbersome to wear. Such a

The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger—the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.

Abstract:The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within-and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.

Substantial agreement was observed between recordings derived from a new ear-EEG sensor and conventional scalp electrodes on four healthy volunteers during daytime naps.

Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

Objective. Smart hearing aids which can decode the focus of a user's attention could considerably improve comprehension levels in noisy environments. Methods for decoding auditory attention from electroencephalography (EEG) have attracted considerable interest for this reason. Recent studies suggest that the integration of deep neural networks (DNNs) into existing auditory attention decoding algorithms is highly beneficial, although it remains unclear whether these enhanced algorithms can perform robustly in different real-world scenarios. To this end, we sought to characterise the performance of DNNs at reconstructing the envelope of an attended speech stream from EEG recordings in different listening conditions. In addition, given the relatively sparse availability of EEG data, we investigate possibility of applying subject-independent algorithms to EEG recorded from unseen individuals. Approach. Both linear models and nonlinear DNNs were employed to decode the envelope of clean speech from EEG recordings, with and without subject-specific information. The mean behaviour, as well as the variability of the reconstruction, was characterised for each model. We then trained subject-specific linear models and DNNs to reconstruct the envelope of speech in clean and noisy conditions, and investigated how well they performed in different listening scenarios. We also established that these models can be used to decode auditory attention in competing-speaker scenarios. Main results. The DNNs offered a considerable advantage over their linear counterpart at reconstructing the envelope of clean speech. This advantage persisted even when subject-specific information was unavailable at the time of training. The same DNN architectures generalised to a distinct dataset, which contained EEG recorded under a variety of listening conditions. In competing-speakers and speech-in-noise conditions, the DNNs significantly outperformed the linear models. Finally, the DNNs offered a considerable improvement over the linear approach at decoding auditory attention in competing-speakers scenarios. Significance. We present the first detailed study into the extent to which DNNs can be employed for reconstructing the envelope of an attended speech stream. We conclusively demonstrate that DNNs have the ability to improve the reconstruction of the attended speech envelope. The variance of the reconstruction error is shown to be similar for both DNNs and the linear model. Overall, DNNs are demonstrated to show promise for real-world auditory attention decoding, since they perform well in multiple listening conditions and generalise to data recorded from unseen participants.