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.Objective. The aim of this work is to demonstrate that such cross-talk is intrinsic to the geometric relationship between the orientation of the verging ocular globes and the planar displacement of the gaze target with respect to the primary gaze position.Approach. Since it is difficult to record actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes, this is studied by simulating the EOG potential values for various horizontally- and vertically-displacing targets using a dipole model of the eye.Main results. We show that cross-talk between the horizontal and vertical bipolar EOG channels is manifested even if the electrodes are aligned with the ocular rotation axes. Specifically, for a horizontally- (vertically-)displaced target, while the monopolar EOG signals obtained from the horizontally- (vertically-)aligned electrodes exhibit an expected predominant potential displacement, a smaller displacement is also exhibited in the monopolar EOG signals obtained from the vertically- (horizontally-)aligned electrodes. These unexpected displacements in the vertically- (horizontally-)aligned monopolar channels may have different magnitudes, resulting in an effective potential displacement in the vertical (horizontal) bipolar EOG channel.Significance. This is significant as it shows that, unlike in many works published so far for EOG-based ocular pose estimation, it is not sufficient to only use the horizontal (vertical) bipolar EOG channel to estimate the horizontal (vertical) displacement of the ocular pose.

Reliability of Upper Limb Pin-Prick Stimulation With Electroencephalography: Evoked Potentials, Spectra and Source Localization.

In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation. We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76-0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81-0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting.

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. We show that a single lightweight metadata insertion block together with a degradation prediction mechanism can allow non-blind SR architectures to rival or outperform state-of-the-art dedicated blind SR networks. We implement various contrastive and iterative degradation prediction schemes and show they are readily compatible with high-performance SR networks such as RCAN and HAN within our framework. Furthermore, we demonstrate our framework's robustness by successfully performing blind SR on images degraded with blurring, noise and compression. This represents the first explicit combined blind prediction and SR of images degraded with such a complex pipeline, acting as a baseline for further advancements.

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. Specifically, it uses the target gaze angles (GAs) in a battery model of the eye to estimate the ocular pose-induced component, which is then used for baseline drift estimation. This method retains the overall signal morphology and may be applied to non-zero-centred data. The performance of the proposed baseline drift mitigation technique is compared to that of five other techniques which are commonly used in the literature, with results showing the general superior performance of the proposed technique.

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. Furthermore, the training-based algorithm is continuously re-trained in real-time. The performance of the SAT framework reached that of training-based algorithms for 8 out of 10 subjects after an average of 179 s ±33 s, an overall improvement over the training-free algorithm of 8.06%.

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. While different contraction type relationships have been studied separately, in this work anisometric and isometric contraction experiments on the biceps brachii muscle were carried out using the same sEMG electrode system and the motor unit activity was then related to limb velocities and limb forces, to respectively characterize the isometric and anisometric contractions. This muscle was chosen as a simpler alternative to the synergistic hand muscles as an initial test of the general concept.Clinical Relevance- These contraction characterizations with sEMG may be used to afford prosthetic intuitive control and to assist in motor impairment diagnosis and rehabilitation.

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. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors' utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.

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. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.

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. We conducted preclinical tests (126 measurements on 42 volunteers) to evaluate the practical capabilities of our device. Two human experts visually assessed the quality of the pressure pulsation and discriminability among various test conditions (specifically, subject lying, sitting, and the Matas carotid occlusion test). RESULTS: The results showed that our device provided clinically relevant outcomes with a sufficient level of detail of the pulse wave and a high reliability (not less than 85%) in all clinically relevant situations. It was possible to record the effect of the Matas carotid occlusion test. COMPARISON WITH EXISTING METHOD(S): Our fully noninvasive, lightweight (185 g), portable, and wireless device provides a considerably cheaper alternative to the current diagnostic methods (e.g., transcranial ultrasound, X-ray, or MRI angiography) for specific assessment of cerebral circulation. Within a minute, it can detect the Willis circle integrity and thus eliminate the potential risks associated with the Matas test using standard EEG. CONCLUSIONS: Our device represents an improvement and a valid alternative to the current methods diagnosing regional cerebral circulation.

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. RESEARCH QUESTION: This study presents a new approach for the estimation of the trajectories of a multi-segment foot model by means of multiple IMUs. METHODS: To test the proposed method, a system consisting of four IMUs attached to the shank, heel, dorsum and toes segments of the foot, was considered. The performance of the proposed method was compared to that of a conventional method using IMUs adopted from the literature. In addition, an optical motion capture system was used as a reference to assess the performance of the implemented methods. RESULTS: Employing the suggested method, all trajectory directions of the shank, heel and dorsum segments, as well as the Z (yaw) direction of the toes segment, have exhibited an error reduction varying between 8% and 55%. However, X (roll) and Y (pitch) direction of the toes segment presented an error increase of 17% and 26%, respectively. The estimation of the vertical displacement, corresponding to the foot clearance, was improved for all segments, resulting in a final mean accuracy and precision of 3.5 ±â€¯2.8 cm, 2.7 ±â€¯2.1 cm, 0.8 ±â€¯0.7 cm and 1.1 ±â€¯0.9 cm for the shank, heel, dorsum and toes segments, respectively. SIGNIFICANCE: It has been demonstrated that as an alternative to tracking each foot segment separately, the fusion of multiple IMU measurements using kinematic equations, considerably improves the estimated trajectories, especially when considering vertical foot displacements. The proposed method could complement the use of smaller and cheaper sensors, while still matching the same performance of other published methods, making the suggested approach very attractive for real life applications.

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. A contactless approach for measuring heart rate could thus prove significant benefits over conventional, contact-based devices. This paper presents a method for the contactless extraction of heart rate measurements from a video footage using principal component analysis, with no pre-defined region of interest being required. Three different ways of presenting the outcome from principal component analysis are presented and the results obtained are discussed.

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 2.42±0.91° and 2.30±0.50° respectively was obtained across six subjects, demonstrating that the proposed methods and the battery model may be used to estimate the user's ocular pose reliably.

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. In this work, a triangulation-based video registration technique using local affine transformations is proposed to register the abdominal region in dynamic thermal sequences. The proposed method is tested on one hour recordings of thermal data obtained from 10 pregnant and 10 non-pregnant female participants. The results obtained show that the proposed approach can compensate for movements and significantly improve region alignment throughout the thermal image sequence, thereby facilitating subsequent analysis of spatiotemporal temperature data in the considered image sequence.

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. Thermographic images from 43 neuroischemic feet, 21 healthy feet, and 12 neuroischemic feet with active ulcer in one of the toes were analyzed. There was a significant difference in toe temperatures between the 3 groups ( P = .001), that is, nonulcerated neuroischemic (n = 181; mean temperature = 27.7°C [±2.16 SD]) versus neuroischemic ulcerated (n = 12; mean temperature = 28.7°C [±3.23 SD]), and healthy T2DM group (n = 104; mean temperature = 24.9°C [±5.04 SD]). A post hoc analysis showed a significant difference in toe temperatures between neuroischemic nonulcerated and healthy T2DM groups ( P = .001), neuroischemic ulcerated and healthy groups ( P = .001). However, no significant differences in toe temperatures were identified between the ulcerated neuroischemic and nonulcerated neuroischemic groups ( P = .626). There were no significant differences between the ulcerated toes (n = 12) and the nonulcerated toes (n = 57) of the same foot in the ulcerated neuroischemic group ( P = .331). Toe temperatures were significantly higher in neuroischemic feet with or without ulceration compared with healthy feet in patients with T2DM. There were no significant differences in temperatures of ulcerated toes and the nonulcerated toes of the same foot, implying that all the toes of the same foot could potentially be at risk of developing complications, which can be potentially detected by infrared thermography.

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. RESULTS: 43 neuroischaemic feet, 41 neuropathic feet, 58 PAD feet, 21 DM feet without complications, and 126 healthy feet were analyzed. The temperatures of the feet and toes were significantly higher in the complications group when compared to the healthy adult and DM healthy groups. The higher the temperatures of the foot in DM, the higher the probability that it is affected by neuropathy, neuroischaemia, or PAD. CONCLUSIONS: Significant differences in mean temperatures exist between participants who were healthy and those with DM with no known complications when compared to participants with neuroischaemia, neuropathy, or PAD. As foot temperature rises, so does the probability of the presence of complications of neuropathy, neuroischaemia, or peripheral arterial disease.

The identification of higher forefoot temperatures associated with peripheral arterial disease in type 2 diabetes mellitus as detected by thermography.

AIMS: The purpose of this study was to investigate whether heat emitted from the feet of patients with type 2 diabetes (DM) and peripheral arterial disease (PAD) differed from those with type 2 diabetes without complications (DM). METHODS: A non-experimental, comparative prospective study design was employed in a tertiary referral hospital. Out of 223 randomly selected participants (430 limbs) who were initially tested, 62 limbs were categorized as DM+PAD and 22 limbs as DM without PAD. Subjects with evidence of peripheral neuropathy were excluded. Participants underwent thermographic imaging. Automatic segmentation of regions of interest extracted the temperature data. RESULTS: A significant difference in temperature in all the toes between the two groups was found (p=0.005, p=0.033, p=0.015, p=0.038 and p=0.02 for toes 1-5 respectively). The mean forefoot temperature in DM+PAD was significantly higher than that in DM (p=.019), with DM+PAD having a higher mean temperature (28.3°C) compared to DM (26.2°C). Similarly, the toes of subjects with DM+PAD were significantly warmer than those of subjects with DM only. CONCLUSIONS: Contrary to expectations the mean toe and forefoot temperatures in DM patients with PAD is higher than in those with DM only. This unexpected result could be attributed to disruption of noradrenergic vasoconstrictor thermoregulatory mechanisms with resulting increased flow through cutaneous vessels and subsequent increased heat emissivity. These results demonstrate that thermography may have potential in detecting PAD and associated temperature differences.

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. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.

Phase-based SSVEPs for real-time control of a motorised bed.

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

Automated segmentation of regions of interest from thermal images of hands.

Thermal imaging can provide an image of the surface temperature of an object in a non-contact and noninvasive manner, making it particularly appealing for use in medical applications. In applications where it is desirable to extract temperature data from anatomical regions of interest (ROIs) in a standardised and consistent manner, the use of automated segmentation and analysis techniques can provide a faster, more reliable and more consistent approach than manual segmentation of these ROIs. In this paper we present an algorithm which automatically extracts temperature data from eight ROIs in thermal images of the volar aspect of human hands. The algorithm first identifies the hand from the background in the thermal image and then identifies pixels which make up the fingers and the palm. Finally, eight ROIs are extracted from the identified regions. The methods proposed in this work can also be extended for the processing of similar visual images.