Agreement of driving simulator and on-road driving performance in patients with binocular visual field loss.

PURPOSE: On-road testing is considered the standard for assessment of driving performance; however, it lacks standardization. In contrast, driving simulators provide controlled experimental settings in a virtual reality environment. This study compares both testing conditions in patients with binocular visual field defects due to bilateral glaucomatous optic neuropathy or due to retro-chiasmal visual pathway lesions. METHODS: Ten glaucoma patients (PG), ten patients with homonymous visual field defects (PH), and 20 age- and gender-matched ophthalmologically normal control subjects (CG and CH, respectively) participated in a 40-min on-road driving task using a dual brake vehicle. A subset of this sample (8 PG, 8 PH, 8 CG, and 7 CH) underwent a subsequent driving simulator test of similar duration. For both settings, pass/fail rates were assessed by a masked driving instructor. RESULTS: For on-road driving, hemianopia patients (PH) and glaucoma patients (PG) showed worse performance than their controls (CH and CG groups): PH 40%, CH 30%, PG 60%, CG 0%, failure rate. Similar results were obtained for the driving simulator test: PH 50%, CH 29%, PG 38%, CG 0%, failure rate. Twenty-four out of 31 participants (77%) showed concordant results with regard to pass/fail under both test conditions (p > 0.05; McNemar test). CONCLUSIONS: Driving simulator testing leads to results comparable to on-road driving, in terms of pass/fail rates in subjects with binocular (glaucomatous or retro-chiasmal lesion-induced) visual field defects. Driving simulator testing seems to be a well-standardized method, appropriate for assessment of driving performance in individuals with binocular visual field loss.

Automated closed-loop management of body temperature using forced-air blankets: preliminary feasibility study in a porcine model.

BACKGROUND: Management of a patient's body temperature is an important aspect of care that should be addressed by targeted temperature management (TTM). Often, non-invasive methods like forced-air blankets are used. Especially in the operating room this management may be a subsidiary and repetitive task requiring constant observation of the patient's body temperature and adaption using the limited set of available settings. Thus, automation of TTM is a feasible target to improve patient outcome and reduce caregiver workload. METHODS: A Philips IntelliVue MP 50 patient monitor with an arterial PiCCO catheter system was used to measure patient blood temperature. Thermal management was performed with a 3M Bair Hugger 755 warming unit with forced air blankets. The warming unit was extended by a computer interface to allow for remote and automated control. A proposed closed-loop algorithm reads the measured temperature and performs automated control of the 3M Bair Hugger. Evaluation was performed in an experimental intensive care setting for animal studies. Two fully automated trials are compared with two manual and two uncontrolled trials in the same study setting using six female pigs for prolonged observation times of up to 90 hours in each trial. RESULTS: The developed system and proposed algorithm allow more precise temperature management by keeping a set target temperature within a range of ± 0.5 °C in 88% of the observation time and within a range of ± 1.0 °C at all times. The proposed algorithm yielded better performance than did manual control or uncontrolled trials. It was able to adapt to individual patient needs as it is more dynamic than look-up table approaches with fixed settings for various temperatures. CONCLUSIONS: Closed-loop TTM using non-invasive forced-air warming blankets was successfully tested in a porcine study with the proposed hardware interface and control algorithm. This automation can be beneficial for patient outcome and can reduce caregiver workload and patient risk in clinical settings. As temperature readings are most often available, existing devices like the 3M Bair Hugger can easily be expanded. However, even if clinical application is feasible, open questions regarding approval and certification of such automated systems within the current legal situation still need to be answered.

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies.

Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: http://www.ti.uni-tuebingen.de/scanpathcomparison.html .

Driving with Glaucoma: Task Performance and Gaze Movements.

PURPOSE: The aim of this pilot study was to assess the driving performance and the visual search behavior, that is, eye and head movements, of patients with glaucoma in comparison to healthy-sighted subjects during a simulated driving test. METHODS: Driving performance and gaze behavior of six glaucoma patients and eight healthy-sighted age- and sex-matched control subjects were compared in an advanced driving simulator. All subjects underwent a 40-minute driving test including nine hazardous situations on city and rural roads. Fitness to drive was assessed by a masked driving instructor according to the requirements of the official German driving test. Several driving performance measures were investigated: lane position, time to line crossing, and speed. Additionally, eye and head movements were tracked and analyzed. RESULTS: Three out of six glaucoma patients passed the driving test and their driving performance was indistinguishable from that of the control group. Patients who passed the test showed an increased visual exploration in comparison to patients who failed; that is, they showed increased number of head and gaze movements toward eccentric regions. Furthermore, patients who failed the test showed a rightward bias in average lane position, probably in an attempt to maximize the safety margin to oncoming traffic. CONCLUSIONS: Our study suggests that a considerable subgroup of subjects with binocular glaucomatous visual field loss shows a safe driving behavior in a virtual reality environment, because they adapt their viewing behavior by increasing their visual scanning. Hence, binocular visual field loss does not necessarily influence driving safety. We recommend that more individualized driving assessments, which will take into account the patient's ability to compensate, are required.

Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data.

BACKGROUND AND OBJECTIVE: Fetal magnetoencephalography (fMEG) is a method for recording fetal brain signals, fetal and maternal heart activity simultaneously. The identification of the R-peaks of the heartbeats forms the basis for later heart rate (HR) and heart rate variability (HRV) analysis. The current procedure for the evaluation of fetal magnetocardiograms (fMCG) is either semi-automated evaluation using template matching (SATM) or Hilbert transformation algorithm (HTA). However, none of the methods available at present works reliable for all datasets. METHODS: Our aim was to develop a unitary, responsive and fully automated R-peak detection algorithm (FLORA) that combines and enhances both of the methods used up to now. RESULTS: The evaluation of all methods on 55 datasets verifies that FLORA outperforms both of these methods as well as a combination of the two, which applies in particular to data of fetuses at earlier gestational age. CONCLUSION: The combined analysis shows that FLORA is capable of providing good, stable and reproducible results without manual intervention.

Fully Automated Subtraction of Heart Activity for Fetal Magnetoencephalography Data.

Fetal magnetoencephalography (fMEG) is a method to record human fetal brain signals in pregnant mothers. Nevertheless the amplitude of the fetal brain signal is very small and the fetal brain signal is overlaid by interfering signals mainly caused by maternal and fetal heart activity. Several methods are used to attenuate the interfering signals for the extraction of the fetal brain signal. However currently used methods are often affected by a reduction of the fetal brain signal or redistribution of the fetal brain signal. To overcome this limitation we developed a new fully automated procedure for removal of heart activity (FAUNA) based on Principal Component Analysis (PCA) and Ridge Regression. We compared the results with an orthogonal projection (OP) algorithm which is widely used in fetal research. The analysis was performed on simulated data sets containing spontaneous and averaged brain activity. The new analysis was able to extract fetal brain signals with an increased signal to noise ratio and without redistribution of activity across sensors compared to OP. The attenuation of interfering heart signals in fMEG data was significantly improved by FAUNA and supports fully automated evaluation of fetal brain signal.

Voluntary brain regulation and communication with electrocorticogram signals.

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

Automatic cluster detection in Kohonen's SOM.

Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM. In this paper, this problem is addressed by introducing an enhanced version of the Clusot algorithm. This algorithm consists of two main steps: 1) the computation of the Clusot surface utilizing the information contained in a trained SOM and 2) the automatic detection of clusters in this surface. In the Clusot surface, clusters present in the underlying SOM are indicated by the local maxima of the surface. For SOMs with 2-D topology, the Clusot surface can, therefore, be considered as a convenient visualization technique. Yet, the presented approach is not restricted to a certain type of 2-D SOM topology and it is also applicable for SOMs having an n-dimensional grid topology.

The effect of monitor raster latency on VEPs, ERPs and Brain-Computer Interface performance.

BACKGROUND: Visual neuroscience experiments and Brain-Computer Interface (BCI) control often require strict timings in a millisecond scale. As most experiments are performed using a personal computer (PC), the latencies that are introduced by the setup should be taken into account and be corrected. As a standard computer monitor uses a rastering to update each line of the image sequentially, this causes a monitor raster latency which depends on the position, on the monitor and the refresh rate. NEW METHOD: We technically measured the raster latencies of different monitors and present the effects on visual evoked potentials (VEPs) and event-related potentials (ERPs). Additionally we present a method for correcting the monitor raster latency and analyzed the performance difference of a code-modulated VEP BCI speller by correcting the latency. COMPARISON WITH EXISTING METHODS: There are currently no other methods validating the effects of monitor raster latency on VEPs and ERPs. RESULTS: The timings of VEPs and ERPs are directly affected by the raster latency. Furthermore, correcting the raster latency resulted in a significant reduction of the target prediction error from 7.98% to 4.61% and also in a more reliable classification of targets by significantly increasing the distance between the most probable and the second most probable target by 18.23%. CONCLUSIONS: The monitor raster latency affects the timings of VEPs and ERPs, and correcting resulted in a significant error reduction of 42.23%. It is recommend to correct the raster latency for an increased BCI performance and methodical correctness.

Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning.

OBJECTIVE: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches. METHODS: An existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroencephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis. RESULTS: The SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power. SIGNIFICANCE: In this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.

Fully automated life support: an implementation and feasibility pilot study in healthy pigs.

BACKGROUND: Automated systems are available in various application areas all over the world for the purpose of reducing workload and increasing safety. However, such support systems that would aid caregivers are still lacking in the medical sector. With respect to workload and safety, especially, the intensive care unit appears to be an important and challenging application field. Whereas many closed-loop subsystems for single applications already exist, no comprehensive system covering multiple therapeutic aspects and interactions is available yet. This paper describes a fully closed-loop intensive care therapy and presents a feasibility analysis performed in three healthy pigs over a period of 72 h each to demonstrate the technical and practical implementation of automated intensive care therapy. METHODS: The study was performed in three healthy, female German Landrace pigs under general anesthesia with endotracheal intubation. An arterial and a central venous line were implemented, and a suprapubic urinary catheter was inserted. Electrolytes, glucose levels, acid-base balance, and respiratory management were completely controlled by an automated fuzzy logic system based on individual targets. Fluid management by adaption of the respective infusion rates for the individual parameters was included. RESULTS: During the study, no manual modification of the device settings was allowed or required. Homoeostasis in all animals was kept stable during the entire observation period. All remote-controlled parameters were maintained within physiological ranges for most of the time (free arterial calcium 73%, glucose 98%, arterial base excess 89%, and etCO2 98%). Subsystem interaction was analyzed. CONCLUSIONS: In the presented study, we demonstrate the feasibility of a fully closed-loop system, for which we collected high-resolution data on the interaction and response of the different subsystems. Further studies should use big data approaches to analyze and investigate the interactions between the subsystems in more detail.

Real-time fetal heart monitoring in biomagnetic measurements using adaptive real-time ICA.

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been named Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics.

AIM: To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample dating. METHODS: Blood pressure information obtained from a patient monitor was fed as a real-time data stream to an experimental medical framework. This framework was connected to an analytical application which observes changes in systolic, diastolic and mean pressure to determine anomalies in the continuous data stream. Detection was based on an increased mean blood pressure caused by the closing of the withdrawal three-way tap and an absence of systolic and diastolic measurements during this manipulation. For evaluation of the proposed algorithm, measured data from animal studies in healthy pigs were used. RESULTS: Using this novel approach for processing real-time measurement data of arterial pressure monitoring, the exact time of blood withdrawal could be successfully detected retrospectively and in real-time. The algorithm was able to detect 422 of 434 (97%) blood withdrawals for blood gas analysis in the retrospective analysis of 7 study trials. Additionally, 64 sampling events for other procedures like laboratory and activated clotting time analyses were detected. The proposed algorithm achieved a sensitivity of 0.97, a precision of 0.96 and an F1 score of 0.97. CONCLUSION: Arterial blood pressure monitoring data can be used to perform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety.

Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection.

Eye-tracking technology has to date been primarily employed in research. With recent advances in affordable video-based devices, the implementation of gaze-aware smartphones, and marketable driver monitoring systems, a considerable step towards pervasive eye-tracking has been made. However, several new challenges arise with the usage of eye-tracking in the wild and will need to be tackled to increase the acceptance of this technology. The main challenge is still related to the usage of eye-tracking together with eyeglasses, which in combination with reflections for changing illumination conditions will make a subject "untrackable". If we really want to bring the technology to the consumer, we cannot simply exclude 30% of the population as potential users only because they wear eyeglasses, nor can we make them clean their glasses and the device regularly. Instead, the pupil detection algorithms need to be made robust to potential sources of noise. We hypothesize that the amount of dust and dirt on the eyeglasses and the eye-tracker camera has a significant influence on the performance of currently available pupil detection algorithms. Therefore, in this work, we present a systematic study of the effect of dust and dirt on the pupil detection by simulating various quantities of dirt and dust on eyeglasses. Our results show 1) an overall high robustness to dust in an offfocus layer. 2) the vulnerability of edge-based methods to even small in-focus dust particles. 3) a trade-off between tolerated particle size and particle amount, where a small number of rather large particles showed only a minor performance impact.

Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task.

OBJECTIVE: In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. APPROACH: A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. MAIN RESULTS: Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. SIGNIFICANCE: The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton.

Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.

EEG Responses to Auditory Stimuli for Automatic Affect Recognition.

Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies.

Comparing metrics to evaluate performance of regression methods for decoding of neural signals.

The use of regression methods for decoding of neural signals has become popular, with its main applications in the field of Brain-Machine Interfaces (BMIs) for control of prosthetic devices or in the area of Brain-Computer Interfaces (BCIs) for cursor control. When new methods for decoding are being developed or the parameters for existing methods should be optimized to increase performance, a metric is needed that gives an accurate estimate of the prediction error. In this paper, we evaluate different performance metrics regarding their robustness for assessing prediction errors. Using simulated data, we show that different kinds of prediction error (noise, scaling error, bias) have different effects on the different metrics and evaluate which methods are best to assess the overall prediction error, as well as the individual types of error. Based on the obtained results we can conclude that the most commonly used metrics correlation coefficient (CC) and normalized root-mean-squared error (NRMSE) are well suited for evaluation of cross-validated results, but should not be used as sole criterion for cross-subject or cross-session evaluations.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression.

BACKGROUND: Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. RESULTS: For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. CONCLUSIONS: Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data.