BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study.

Purpose: Brain-computer interface (BCI)-controlled wheelchairs have the potential to improve the independence of people with mobility impairments. The low uptake of BCI devices has been linked to a lack of knowledge among researchers of the needs of end-users that should influence BCI development.

Materials And Methods: This study used semi-structured interviews to learn about the perceptions, needs, and expectations of spinal cord injury (SCI) patients with regards to a BCI-controlled wheelchair.

The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks.

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution-high-resolution (LR-HR) pairs or (B) predict the degradations of an LR image and then use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information and followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network.

Monopolar and bipolar electrooculography signal characteristics due to target displacements-have we seen the whole picture?

The development of electrooculography (EOG)-based human-computer interface systems is generally based on the processing of the commonly referred to horizontal and vertical bipolar EOG channels, which are computed from a horizontally-aligned and another vertically-aligned pair of electrodes, respectively. Horizontal (vertical) target displacements are assumed to result in changes in the horizontal (vertical) EOG channel only, and any cross-talk between the bipolar channels is often neglected or incorrectly attributed solely to electrode misalignment with respect to the ocular rotation axes..

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices.

Isometric and Anisometric Contraction Relationships with Surface Electromyography.

The isometric contraction is the most investigated muscle contraction, however most tasks in daily life involve anisometric contractions. Most hand prostheses studies [1] use sEMG features to directly relate the exerted force as a means of intuitive control. It may thus be expected that similar sEMG-velocity relationships characterizing anisometric contractions may also contribute towards intuitive prosthetic hand control.

SAT: A Switch-And-Train Framework for Real-Time Training of SSVEP-based BCIs.

Reducing the training time for brain computer interfaces based on steady state evoked potentials, is essential to develop practical applications. We propose to eliminate the training required by the user before using the BCI with a switch-and-train (SAT) framework. Initially the BCI uses a training-free detection algorithm, and once sufficient training data is collected online, the BCI switches to a subject-specific training-based algorithm.

TEMoD: Target-Enabled Model-Based De-Drifting of the EOG Signal Baseline using a Battery Model of the Eye.

The electrooculography (EOG) signal baseline is subject to drifting, and several different techniques to mitigate this drift have been proposed in the literature. Some of these techniques, however, disrupt the overall ocular pose-induced DC characteristics of the EOG signal and may also require the data to be zero-centred, which means that the average point of gaze (POG) has to lie at the primary gaze position. In this work, we propose an alternative baseline drift mitigation technique which may be used to de-drift EOG data collected through protocols where the subject gazes at known targets.

Principal component analysis of hyperspectral data for early detection of mould in cheeselets.

The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples' surface.

Bacterial and fungal contaminants in caprine and ovine cheese: A meta-analysis assessment.

The dairy industry is of great importance to the European economy contributing towards € 8.7 billion of the total trade surplus. Caprine and ovine milk amount to 3.

A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis.

The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors' utilization; (3) foot pose and trajectory estimation methods.

EOG-Based Gaze Angle Estimation Using a Battery Model of the Eye.

In this work, a novel method to estimate the gaze angles using electrooculographic (EOG) signals is presented. Specifically, this work investigates the use of a battery model of the eye, which relates the recorded EOG potential with the distances between the corresponding electrode and the centre points of the cornea and retina, for gaze angle estimation. Using this method a cross-validated horizontal and vertical gaze angle error of 2.

PCA-driven Detection and Enhancement of Microchanges in Video Data Associated with Heart Rate.

The heart rate is a fundamental measure which can be used to monitor an individual's level of health or fitness, as well as a range of medical conditions. Conventional heart rate devices used in hospitals require continuous contact with specific points on the patient's body, depending on the device being used. Such continuous contact could prove to be a risk for skin irritation or infections and may also be of inconvenience to the patients, potentially restricting movement.

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks.

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary.

Digital orbitoplethysmograph: A new device to study the regional cerebral circulation using extraorbital plethysmography.

Background: Noninvasive diagnostic methods utilizing pulse wave measurements on the surface of the head are an important tool in diagnosing various types of cerebrovascular disease. The measurement of extraorbital pressure fluctuations reflects intraocular and intracranial pressure changes and can be used to estimate pressure changes in intracranial arteries and the collateral circulation.

New Method: In this paper, we describe our patented (CZ 305757) digital device for noninvasive measuring and monitoring of orbital movements using pressure detection.

A multi-segment modelling approach for foot trajectory estimation using inertial sensors.

Background: Kinematic gait analysis employing multi-segment foot models has been mainly conducted in laboratories by means of optical motion capture systems. This type of process requires considerable setup time and is constrained by a limited capture space. A procedure involving the use of multiple inertial measurement units (IMUs) is proposed to overcome these limitations.

Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring.

This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants.

Registration of Dynamic Thermography Data of the Abdomen of Pregnant and Non-Pregnant Women.

To date the use of thermography in the context of obstetrics has been primarily limited to the acquisition and analysis of static thermal images. In contrast, dynamic thermography involves the acquisition of a sequence of thermal images, taking into account temporal variations that would otherise be overlooked. However, dynamic recordings of regions of interest in human participants are likely to be affected by unavoidable participant movement due to breathing and other involuntary movements.

Principal Component Analysis of Dynamic Thermography Data from Pregnant and Non-Pregnant Women.

In this work we propose a novel approach for the analysis ofdynamic thermography data based on the application of principal component analysis to thermal video data. The proposed approach is applied to thermal video recordings of the abdominal region of pregnant and non-pregnant female participants, and reveals consistent temperature trends across participants that to date have not been reported. Both for the pregnant and non-pregnant participants, the first principal component was found to describe approximately 80% of the total variance, and when combined, the first three principal components explained more than 90% of the total variance.

A systematic review investigating the relationship of electroencephalography and magnetoencephalography measurements with sensorimotor upper limb impairments after stroke.

Background: Predicting sensorimotor upper limb outcome receives continued attention in stroke. Neurophysiological measures by electroencephalography (EEG) and magnetoencephalography (MEG) could increase the accuracy of predicting sensorimotor upper limb recovery.

New Method: The aim of this systematic review was to summarize the current evidence for EEG/MEG-based measures to index neural activity after stroke and the relationship between abnormal neural activity and sensorimotor upper limb impairment.

The Application of Medical Thermography to Discriminate Neuroischemic Toe Ulceration in the Diabetic Foot.

This study aimed to determine whether thermal imaging can detect temperature differences between healthy feet, nonulcerated neuroischemic feet, and neuroischemic feet with toe ulcers in patients with type 2 diabetes mellitus (T2DM). Participants were prospectively divided into 3 groups: T2DM without foot problems; a healthy, nonulcerated neuroischemic group, and an ulcerated neuroischemic group. Thermal images of the feet were obtained with automated segmentation of regions of interest.

Establishing Differences in Thermographic Patterns between the Various Complications in Diabetic Foot Disease.

Aim: To evaluate the potential of thermography as an assessment tool for the detection of foot complications by understanding the variations in temperature that occur in type 2 diabetes mellitus (DM).

Methods: Participants were categorized according to a medical examination, ankle brachial index, doppler waveform analysis, and 10-gram monofilament testing into five groups: healthy adult, DM with no complications, DM with peripheral neuropathy, DM with neuroischaemia, and DM with peripheral arterial disease (PAD) groups. Thermographic imaging of the toes and forefeet was performed.

Working memory alpha-beta band oscillatory signatures in adolescents and young adults.

The timing of neural activity is an intriguing way of exposing behaviorally relevant neural activity, as neural populations exploit transient windows of synchronized activations to exchange dynamic communications in the service of various cognitive operations. The link between neural synchrony and working memory (WM) has been supported at the theoretical and empirical level. However, findings have also shown that WM encoding is also related to significant alpha-beta desynchronization.