Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution.

High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised.

Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study.

The switching and learning behavior of an octopus cell implemented on FPGA.

A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams.

Investigating cognitive-motor effects during slacklining using mobile EEG.

Balancing is a very important skill, supporting many daily life activities. Cognitive-motor interference (CMI) dual-tasking paradigms have been established to identify the cognitive load of complex natural motor tasks, such as running and cycling. Here we used wireless, smartphone-recorded electroencephalography (EEG) and motion sensors while participants were either standing on firm ground or on a slackline, either performing an auditory oddball task (dual-task condition) or no task simultaneously (single-task condition).

Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid.

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available.

Does experience with hearing aid amplification influence electrophysiological measures of speech comprehension?

Objective: To explore if experience with hearing aid (HA) amplification affects speech-evoked cortical potentials reflecting comprehension abilities.

Design: N400 and late positive complex (LPC) responses as well as behavioural response times to congruent and incongruent digit triplets were measured. The digits were presented against stationary speech-shaped noise 10 dB above individually measured speech recognition thresholds.

Evaluation of adjustment behaviour in a semi-supervised self-adjustment fine-tuning procedure for hearing aids.

Objective: This study investigated the adjustment behaviour of hearing aid (HA) users participating in a semi-supervised self-adjustment fine-tuning procedure for HAs. The aim was to link behaviour with the reproducibility and duration of the adjustments.

Design: Participants used a two-dimensional user interface to identify their HA gain preferences while listening to realistic sound scenes presented in a laboratory environment.

Outdoor walking: Mobile EEG dataset from walking during oddball task and walking synchronization task.

This article describes a dataset from one standing and two outdoor walking tasks. Each task was performed by the same 18 participants twice, using foot accelerometers and two different EEG system configurations. The first task was a brief eyes open/eyes closed task.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait.

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs).

Expert validation of prediction models for a clinical decision-support system in audiology.

For supporting clinical decision-making in audiology, Common Audiological Functional Parameters (CAFPAs) were suggested as an interpretable intermediate representation of audiological information taken from various diagnostic sources within a clinical decision-support system (CDSS). Ten different CAFPAs were proposed to represent specific functional aspects of the human auditory system, namely hearing threshold, supra-threshold deficits, binaural hearing, neural processing, cognitive abilities, and a socio-economic component. CAFPAs were established as a viable basis for deriving audiological findings and treatment recommendations, and it has been demonstrated that model-predicted CAFPAs, with machine learning models trained on expert-labeled patient cases, are sufficiently accurate to be included in a CDSS, but it requires further validation by experts.

Automated Speech Audiometry for Integrated Voice Over Internet Protocol Communication Services.

Purpose: Problems in speech recognition are often apparent in telecommunication situations. For ecologically valid assessments of such conditions, it is important to quantify the impact of real environments including acoustic conditions at a far-end communication device and all paths of transmission degradation. This study presents an automated matrix sentence test procedure based on automatic speech recognition (ASR) integrated in a Voice over Internet Protocol (VoIP) infrastructure and compares the individual effects of transmission degradations with results from laboratory measurements.

Pre-gelled Electrode Grid for Self-Applied EEG Sleep Monitoring at Home.

The need for diagnostic capabilities for sleep disorders such as sleep apnea and insomnia far exceeds the capacity of inpatient sleep laboratories. Some home monitoring systems omit electroencephalography (EEG) because trained personnel may be needed to apply EEG sensors. Since EEG is essential for the detailed evaluation of sleep, better systems supporting the convenient and robust recording of sleep EEG at home are desirable.

Ear-EEG Measures of Auditory Attention to Continuous Speech.

Auditory attention is an important cognitive function used to separate relevant from irrelevant auditory information. However, most findings on attentional selection have been obtained in highly controlled laboratory settings using bulky recording setups and unnaturalistic stimuli. Recent advances in electroencephalography (EEG) facilitate the measurement of brain activity outside the laboratory, and around-the-ear sensors such as the cEEGrid promise unobtrusive acquisition.

Evaluation of a semi-supervised self-adjustment fine-tuning procedure for hearing aids.

Objective: This study investigated the effects of different adjustment criteria and sound scenes on self-adjusted hearing-aid gain settings. Self-adjusted settings were evaluated for speech recognition in noise, perceived listening effort, and preference.

Design: This study evaluated a semi-supervised self-adjustment fine-tuning procedure that presents realistic everyday sound scenes in a laboratory environment, using a two-dimensional user interface, and enabling simultaneous changes in amplitude and spectral slope.

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG.

Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding (AAD) methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings.

Flex-Printed Ear-EEG Sensors for Adequate Sleep Staging at Home.

A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep.

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology.

The application of machine learning for the development of clinical decision-support systems in audiology provides the potential to improve the objectivity and precision of clinical experts' diagnostic decisions. However, for successful clinical application, such a tool needs to be accurate, as well as accepted and trusted by physicians. In the field of audiology, large amounts of patients' data are being measured, but these are distributed over local clinical databases and are heterogeneous with respect to the applied assessment tools.

EEG-Based Intersubject Correlations Reflect Selective Attention in a Competing Speaker Scenario.

Several solutions have been proposed to study the relationship between ongoing brain activity and natural sensory stimuli, such as running speech. Computing the intersubject correlation (ISC) has been proposed as one possible approach. Previous evidence suggests that ISCs between the participants' electroencephalogram (EEG) may be modulated by attention.

An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment.

The reproduction and simulation of workplaces, and the analysis of body postures during work processes, are parts of ergonomic risk assessments. A commercial virtual reality (VR) system offers the possibility to model complex work scenarios as virtual mock-ups and to evaluate their ergonomic designs by analyzing motion behavior while performing work processes. In this study a VR tracking sensor system (HTC Vive tracker) combined with an inverse kinematic model (Final IK) was compared with a marker-based optical motion capture system (Qualisys).

Deep Neural Network Driven Speech Classification for Relevance Detection in Automatic Medical Documentation.

The automation of medical documentation is a highly desirable process, especially as it could avert significant temporal and monetary expenses in healthcare. With the help of complex modelling and high computational capability, Automatic Speech Recognition (ASR) and deep learning have made several promising attempts to this end. However, a factor that significantly determines the efficiency of these systems is the volume of speech that is processed in each medical examination.

Are They Calling My Name? Attention Capture Is Reflected in the Neural Tracking of Attended and Ignored Speech.

Difficulties in selectively attending to one among several speakers have mainly been associated with the distraction caused by ignored speech. Thus, in the current study, we investigated the neural processing of ignored speech in a two-competing-speaker paradigm. For this, we recorded the participant's brain activity using electroencephalography (EEG) to track the neural representation of the attended and ignored speech envelope.

OWAS inter-rater reliability.

The Ovako Working posture Assessment System (OWAS) is a commonly used observational assessment method for determining the risk of work-related musculoskeletal disorders. OWAS claims to be suitable in the application for untrained persons but there is not enough evidence for this assumption. In this paper, inter-rater (inter-observer) reliability (agreement) is examined down to the level of individual postures and categories.

Effect of Directional Microphone Technology in Hearing Aids on Neural Correlates of Listening and Memory Effort: An Electroencephalographic Study.

The aim of the study was to compare the effect of different spatial noise-processing algorithms in hearing aids on listening effort and memory effort on a subjective, behavioral, and neurophysiological level using electroencephalography (EEG). Two types of directional microphone (DM) technologies for spatial noise processing were chosen: one with a wide directionality (wide DM) and another with a narrower directionality (narrow DM) to accentuate the speech source. Participants with a severe hearing loss were fitted with hearing aids and participated in two EEG experiments.

Decoding the Attended Speaker From EEG Using Adaptive Evaluation Intervals Captures Fluctuations in Attentional Listening.

Listeners differ in their ability to attend to a speech stream in the presence of a competing sound. Differences in speech intelligibility in noise cannot be fully explained by the hearing ability which suggests the involvement of additional cognitive factors. A better understanding of the temporal fluctuations in the ability to pay selective auditory attention to a desired speech stream may help in explaining these variabilities.