Interhemispheric differences of electroencephalography signal characteristics in different sleep stages

OBJECTIVE

The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages.

METHODS

Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages.

RESULTS

The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages.

CONCLUSIONS

Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals

Carbon nanotube (CNT)-based nanocomposites have found applications in making sensors for various types of physiological sensing. However, the sensors' fabrication process is usually complex, multistep, and requires longtime mixing and hazardous solvents that can be harmful to the environment. Here, we report a flexible dry silver (Ag)/CNT/polydimethylsiloxane (PDMS) nanocomposite-based sensor made by a solvent-free, low-temperature, time-effective, and simple approach for electrophysiological recording. By mechanical compression and thermal treatment of Ag/CNT, a connected conductive network of the fillers was formed, after which the PDMS was added as a polymer matrix. The CNTs make a continuous network for electrons transport, endowing the nanocomposite with high electrical conductivity, mechanical strength, and durability. This process is solvent-free and does not require a high temperature or complex mixing procedure. The sensor shows high flexibility and good conductivity. High-quality electroencephalography (EEG) and electrooculography (EOG) were performed using fabricated dry sensors. Our results show that the Ag/CNT/PDMS sensor has comparable skin-sensor interface impedance with commercial Ag/AgCl-coated dry electrodes, better performance for noninvasive electrophysiological signal recording, and a higher signal-to-noise ratio (SNR) even after 8 months of storage. The SNR of electrophysiological signal recording was measured to be 26.83 dB for our developed sensors versus 25.23 dB for commercial Ag/AgCl-coated dry electrodes. Our process of compress-heating the functional fillers provides a universal approach to fabricate various types of nanocomposites with different nanofillers and desired electrical and mechanical properties.

A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels

OBJECTIVE

Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance.

METHODS

A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection.

RESULT

The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03±4.65%, an average information transfer rate (ITR) of 31.42±7.39 bits/min and an average false-positive rate (FPR) of 1.78%.

CONCLUSION

The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system.

SIGNIFICANCE

Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

Facial Motion Capture System Based on Facial Electromyogram and Electrooculogram for Immersive Social Virtual Reality Applications

With the rapid development of virtual reality (VR) technology and the market growth of social network services (SNS), VR-based SNS have been actively developed, in which 3D avatars interact with each other on behalf of the users. To provide the users with more immersive experiences in a metaverse, facial recognition technologies that can reproduce the user's facial gestures on their personal avatar are required. However, it is generally difficult to employ traditional camera-based facial tracking technology to recognize the facial expressions of VR users because a large portion of the user's face is occluded by a VR head-mounted display (HMD). To address this issue, attempts have been made to recognize users' facial expressions based on facial electromyogram (fEMG) recorded around the eyes. fEMG-based facial expression recognition (FER) technology requires only tiny electrodes that can be readily embedded in the HMD pad that is in contact with the user's facial skin. Additionally, electrodes recording fEMG signals can simultaneously acquire electrooculogram (EOG) signals, which can be used to track the user's eyeball movements and detect eye blinks. In this study, we implemented an fEMG- and EOG-based FER system using ten electrodes arranged around the eyes, assuming a commercial VR HMD device. Our FER system could continuously capture various facial motions, including five different lip motions and two different eyebrow motions, from fEMG signals. Unlike previous fEMG-based FER systems that simply classified discrete expressions, with the proposed FER system, natural facial expressions could be continuously projected on the 3D avatar face using machine-learning-based regression with a new concept named the virtual blend shape weight, making it unnecessary to simultaneously record fEMG and camera images for each user. An EOG-based eye tracking system was also implemented for the detection of eye blinks and eye gaze directions using the same electrodes. These two technologies were simultaneously employed to implement a real-time facial motion capture system, which could successfully replicate the user's facial expressions on a realistic avatar face in real time. To the best of our knowledge, the concurrent use of fEMG and EOG for facial motion capture has not been reported before.

An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning

Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver's physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver's physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier's performance produced an enhanced accuracy when compared to others.

Jumping Knowledge Based Spatial-temporal Graph Convolutional Networks for Automatic Sleep Stage Classification

A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN model to extract spatial features from the graph convolutions efficiently and temporal features are extracted from its common standard convolutions to learn the transition rules among sleep stages. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively.

A Flexible Wearable Electrooculogram System With Motion Artifacts Sensing and Reduction

Electrooculogram (EOG) is a well-known physiological metric picked up by placing two or more electrodes around the eyeball. EOG signals are known to be extremely susceptible to motion artifacts. This paper presents a single channel, wireless, wearable flexible EOG monitoring system with motion artifacts sensing and reduction feature. The system uses two non-contact electrode pairs for EOG/motion artifacts detection and motion artifacts reduction. It is implemented on a four-layer flexible polyimide substrate. It is light-weight (only 8.75 gram), battery operated, and uses a microcontroller and a BLE 5.0 transceiver for wireless EOG data transmission, while consuming only 56 mW of power. The system metrics such as gain around 37 dB, bandwidth from 1 Hz to 40 Hz, and noise are evaluated. The system is tested for different electrode configurations and it is demonstrated that horizontally parallel electrode pairs achieve an acceptable motion artifact reduction at the output, while preserving perfect EOG features (such as eye-blinking). The average sensitivity for horizontally parallel non-contact electrodes is found out to be more than 50 times with respect to commercial gold electrodes, whereas the average response time of the sensor is around 380 mS. The flexible EOG system is comfortable to wear and the use of non-contact electrode eliminates the need of skin preparation. Therefore, the system can be easily integrated with eye-masks and headbands, thus making it an excellent prototype for many smart applications.

OCULAR MANIFESTATIONS OF ASP38ALA AND THR59LYS FAMILIAL TRANSTHYRETIN AMYLOIDOSIS

PURPOSE

To describe the ophthalmic manifestations of familial transthyretin amyloidosis (FTA) mutations, including Asp38Ala and Thr59Lys, which have not been previously reported to have ocular involvement.

METHODS

This is an observational case series of prospectively collected data of 16 patients with FTA who were taking tafamidis for mild peripheral neuropathy and underwent a comprehensive ophthalmic examination at a single tertiary center, between January 2013 and March 2020. The ocular involvement of each FTA mutation type and the specific manifestations were the main outcome measures.

RESULTS

Six of 16 patients with FTA manifested ocular involvement. Ocular involvement was noted in two of three patients with Glu89Lys mutations having retinal deposits, retinal hemorrhages, and corneal opacity. Three of nine patients with Asp38Ala mutations and one of two patients with Thr59Lys mutations showed ocular involvement that had not been previously described. The ophthalmic findings included glaucoma, anterior lens capsule opacity, vitreous opacity, and retinal deposits. The decrease in vascular flow due to perivascular cuffing of the amyloid deposits was detected by optical coherence tomography angiography.

CONCLUSION

The current study newly described that two transthyretin mutation types of FTA, Asp38Ala and Thr59Lys, may manifest with ocular findings such as anterior lens capsule opacity and retinal deposits.

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.

Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach

Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitive-relevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined frontal-only sensor montages with sufficient signal fidelity. We propose, here, a spatial filtering approach to reliably extract EOG signals from a reduced set of frontal EEG electrodes, placed on non-hair-bearing (NHB) areas. Within a common signal analytic framework, two distinct schemes are examined. The one is based on standard linear least squares (LLS) and the other on Least Absolute Shrinkage and Selection Operator (LASSO). Both schemes are data-driven techniques, require a small amount of training data, and lead to reliable estimators of EOG activity from EEG signals. The LASSO-based technique, in addition, provides guidelines that generalize well across subjects. Using experimental data, we provide some empirical evidence that our estimators can replace the actual EOG signals in algorithmic pipelines that automatically detect oculographic events, like blinks and saccades.

eyeSay: Make Eyes Speak for ALS Patients with Deep Transfer Learning-empowered Wearable

Eye dynamics, a typical expression of brain activities, is an emerging modality for emerging and promising smart health applications. Electrooculogram (EOG) - a natural bio-electric signal generated during eye movements, if decoded, is of great potential to reveal the user's mind and enable voice-free communication for patients with amyotrophic lateral sclerosis (ALS). ALS patients usually lose physical movement abilities including speech and handwriting but fortunately can move their eyes. In this study, we propose a novel deep transfer learning-empowered system, called "eyeSay", which leverages both deep learning and transfer learning for intelligent eye EOG-to-speech translation. More specifically, we have designed a multi-stage convolutional neural network (CNN) to analyze the eye-written words, named as CNN-word. Moreover, to reveal fundamental patterns of eye movements, we build a transferable feature extractor, CNN-stroke, upon eye strokes that are building components of an eye word. Then, we transfer the CNN-stroke model to the eye word learning task in an innovative way, that is, use CNN-stroke as an additional branch of CNN-word to generate a stroke probability map. The achieved boostCNN-word model, enhanced by the transferable feature extractor, has greatly improved the eye word decoding performance. This novel study will directly contribute to voice-free communications for ALS patients, and greatly advance the ubiquitous eye EOG-based smart health area.

Vigilance Estimating in SSVEP-Based BCI Using Multimodal Signals

Brain-computer interface (BCI) is a communication system that allows a direct connection between the human brain and external devices. With the application of BCI, it is important to estimate vigilance for BCI users. In order to investigate the vigilance changes of the subjects during BCI tasks and develop a multimodal method to estimate the vigilance level, a high-speed 4-target BCI system for cursor control was built based on steady-state visual evoked potential (SSVEP). 18 participants were recruited and underwent a 90-min continuous cursor-control BCI task, when electroencephalogram (EEG), electrooculogram (EOG), electrocardiography (ECG), and electrodermal activity (EDA) were recorded simultaneously. Then, we extracted features from the multimodal signals and applied regression models to estimate vigilance. Experimental results showed that the differential entropy (DE) feature could effectively reflect the change of vigilance. The vigilance estimation method, which integrates DE and EOG features into the support vector regression (SVR) model, achieved a better performance than the compared methods. These results demonstrate the feasibility of our methods for estimating vigilance levels in BCI.

Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:2363-2366. [PMID: 34891757 DOI: 10.1109/embc46164.2021.9630506]

Many automated sleep staging studies have used deep learning approaches, and a growing number of them have used multimodal data to improve their classification performance. However, few studies using multimodal data have provided model explainability. Some have used traditional ablation approaches that "zero out" a modality. However, the samples that result from this ablation are unlikely to be found in real electroencephalography (EEG) data, which could adversely affect the importance estimates that result. Here, we train a convolutional neural network for sleep stage classification with EEG, electrooculograms (EOG), and electromyograms (EMG) and propose an ablation approach that replaces each modality with values that approximate the line-related noise commonly found in electrophysiology data. The relative importance that we identify for each modality is consistent with sleep staging guidelines, with EEG being important for most sleep stages and EOG being important for Rapid Eye Movement (REM) and non-REM stages. EMG showed low relative importance across classes. A comparison of our approach with a "zero out" ablation approach indicates that while the importance results are consistent for the most part, our method accentuates the importance of modalities to the model for the classification of some stages like REM (p < 0.05). These results suggest that a careful, domain-specific selection of an ablation approach may provide a clearer indicator of modality importance. Further, this study provides guidance for future research on using explainability methods with multimodal electrophysiology data.Clinical Relevance- While explainability is helpful for clinical machine learning classifiers, it is important to consider how explainability methods interact with clinical data, a domain for which they were not originally designed.

Removing EOG Artifacts from the EEG signal of Methamphetamine Addicts

EEG can be used to characterize the electrical activity of the cerebral cortex, but it is also susceptible to interference. Compared with the other artifacts, Electrooculogram (EOG) artifacts have a greater impact on EEG processing and are more difficult to remove. Here, we mainly compared the regression and ICA algorithms both based on the EOG channels for the effect of removing EOG artifacts in the Stroop experiment of methamphetamine addicts. From the perspective of time domain and power spectral density, the ICA algorithm based on the EOG channels is more effective. However, the regression algorithm based on the EOG channels is less complex, more time-saving, and more suitable for real-time tasks.Clinical Relevance- For clinical purposes, this research has a certain reference value for selecting appropriate methods of removing EOG artifacts when processing the EEG of methamphetamine addicts.

Use of Force Feedback Device in a Hybrid Brain-Computer Interface Based on SSVEP, EOG and Eye Tracking for Sorting Items

Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EEG signal recording devices and ever-lower set prices. Unfortunately, such systems are substantially limited in terms of the number of generated commands. This especially applies to sets that are not medical devices. This article proposes a hybrid brain-computer system based on the Steady-State Visual Evoked Potential (SSVEP), EOG, eye tracking, and force feedback system. Such an expanded system eliminates many of the particular system shortcomings and provides much better results. The first part of the paper presents information on the methods applied in the hybrid brain-computer system. The presented system was tested in terms of the ability of the operator to place the robot’s tip to a designated position. A virtual model of an industrial robot was proposed, which was used in the testing. The tests were repeated on a real-life industrial robot. Positioning accuracy of system was verified with the feedback system both enabled and disabled. The results of tests conducted both on the model and on the real object clearly demonstrate that force feedback improves the positioning accuracy of the robot’s tip when controlled by the operator. In addition, the results for the model and the real-life industrial model are very similar. In the next stage, research was carried out on the possibility of sorting items using the BCI system. The research was carried out on a model and a real robot. The results show that it is possible to sort using bio signals from the human body.

Multimodal analysis of electroencephalographic and electrooculographic signals

Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R² = 1.00 outperforming the other two approaches with R² = 0.80 for DT and R² = 0.76 for SVM.

Evaluation of eyestrain with vertical electrooculogram

BACKGROUND AND OBJECTIVE

Eyestrain has been increasingly severe in our lives and works as the progress of computers and smartphones. Evaluating eyestrain helps to prevent and relieve eyestrain. Our study aimed to evaluate eyestrain by analyzing vertical electrooculogram (VEOG).

METHODS

21 young subjects were asked to watch a video on the computer for a totally 120 minutes each, during which the VEOG signal was acquired using only three electrodes, and the questionnaire was answered every 30 minutes. The VEOG signal was divided into four 30-minute phases, from which VEOG signal power probability (VEOGSPP) features and blink features were extracted. The blink features include the changes of blink number (BN), group blinks number (GBN) and ratio (GBR), mean blink amplitude (Mean_BA) and duration (Mean_BD), mean blink duration at 50% (Mean_BD50), mean closing duration (Mean_CD) and opening duration (Mean_OD), mean opening duration at early 50% (Mean_ODE50) and late 50% (Mean_ODL50), mean blink maximum rising slope (Mean_BMRS) and falling slope (Mean_BMFS).

RESULTS

The results showed that the VEOGSPP in the high-frequency band (0.8-6.3Hz), BN, GBN, and GBR significantly increased while the VEOGSPP in the low-frequency band (0.1-0.4Hz), Mean_BA, Mean_OD, and Mean_ODL50 significantly decreased with eyestrain (P<0.05).

CONCLUSIONS

In conclusion, eyestrain induced by watching videos for a long time could be well evaluated by analyzing the VEOG signal.

Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

People with severe disabilities require assistance to perform their routine activities; a Human-Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user's learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.

Differentiation of Saccadic Eye Movement Signals

Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task.

An Electro-Oculogram Based Vision System for Grasp Assistive Devices-A Proof of Concept Study

Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple ‘signature’ extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.

Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM

To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user's forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young's modulus (286 KPa) and exceptional breathability (18 g m-2 h-1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.

Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art. We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.

Reference ranges for clinical electrophysiology of vision

Introduction

Establishing robust reference intervals for clinical procedures has received much attention from international clinical laboratories, with approved guidelines. Physiological measurement laboratories have given this topic less attention; however, most of the principles are transferable.

Methods

Herein, we summarise those principles and expand them to cover bilateral measurements and one-tailed reference intervals, which are common issues for those interpreting clinical visual electrophysiology tests such as electroretinograms (ERGs), visual evoked potentials (VEPs) and electrooculograms (EOGs).

Results

The gold standard process of establishing and defining reference intervals, which are adequately reliable, entails collecting data from a minimum of 120 suitable reference individuals for each partition (e.g. sex, age) and defining limits with nonparametric methods. Parametric techniques may be used under some conditions. A brief outline of methods for defining reference limits from patient data (indirect sampling) is given. Reference intervals established elsewhere, or with older protocols, can be transferred or verified with as few as 40 and 20 suitable reference individuals, respectively. Consideration is given to small numbers of reference subjects, interpretation of serial measurements using subject-based reference values, multidimensional reference regions and age-dependent reference values. Bilateral measurements, despite their correlation, can be used to improve reference intervals although additional care is required in computing the confidence in the reference interval or the reference interval itself when bilateral measurements are only available from some of subjects.

Discussion

Good quality reference limits minimise false-positive and false-negative results, thereby maximising the clinical utility and patient benefit. Quality indicators include using appropriately sized reference datasets with appropriate numerical handling for reporting; using subject-based reference limits where appropriate; and limiting tests for each patient to only those which are clinically indicated, independent and highly discriminating.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10633-021-09831-1.

Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces

Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user's intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms-one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.

Ictal activation of oxygen-conserving reflexes as a mechanism for sudden death in epilepsy

OBJECTIVE

To test the hypothesis that death with physiological parallels to human cases of sudden unexpected death in epilepsy (SUDEP) can be induced in seizing rats by ictal activation of oxygen-conserving reflexes (OCRs).

METHODS

Urethane-anesthetized female Long-Evans rats were implanted with electrodes for electrocardiography (ECG), electrocorticography (ECoG), and respiratory thermocouple; venous and arterial cannulas; and a laryngoscope guide and cannula or nasal cannula for activation of the laryngeal chemoreflex (LCR) or mammalian diving reflex (MDR), respectively. Kainic acid injection, either systemic or into the ventral hippocampus, induced prolonged acute seizures.

RESULTS

Reflex challenges during seizures caused sudden death in 18 of 20 rats-all MDR rats (10) and all but two LCR rats (8) failed to recover from ictal activation of OCRs and died within minutes of the reflexes. By comparison, 4 of 4 control (ie, nonseizing) rats recovered from 64 induced diving reflexes (16 per rat), and 4 of 4 controls recovered from 64 induced chemoreflexes (16 per rat). Multiple measures were consistent with reports of human SUDEP. Terminal central apnea preceded terminal asystole in all cases. Heart and respiratory rate fluctuations that paralleled those seen in human SUDEP occurred during OCR-induced sudden death, and mean arterial pressure (MAP) was predictive of death, showing a 17 or 15 mm Hg drop (MDR and LCR, respectively) in the 20 s window centered on the time of brain death. OCR activation was never fatal in nonseizing rats.

SIGNIFICANCE

These results present a method of inducing sudden death in two seizure models that show pathophysiology consistent with that observed in human cases of SUDEP. This proposed mechanism directly informs previous findings by our group and others in the field; provides a repeatable, inducible animal model for the study of sudden death; and offers a potential explanation for observations made in cases of human SUDEP.

A Fusion Algorithm for Saccade Eye Movement Enhancement With EOG and Lumped-Element Models

Electrooculography (EOG) can be used to measure eye movements while the eyelids are open or closed and to assist in the diagnosis of certain eye diseases. However, challenges in biosignal acquisition and processing lead to limited accuracy, limited resolution (both temporal and spatial), as well as difficulties in reducing noise and detecting artifacts. Methods such as finite impulse response, wavelet transforms, and averaging filters have been used to denoise and enhance EOG measurements. However, these filters are not specifically designed to detect saccades, and so key features (e.g., saccade amplitude) can be over-filtered and distorted as a consequence of the filtering process. Here we present a model-based fusion technique to enhance saccade features within noisy and raw EOG signals. Specifically, we focus on Westheimer (WH) and linear reciprocal (LR) eye models with a Kalman filter. EOG signals were measured using OpenBCI's Cyton Board (at 250 Hz), and these measurements were compared with a state-of-the-art EyeLink 1000 (EL; 250 Hz) eye tracker. On average, the LR model-based KF produced a 47% improvement of measurement accuracy over the bandpass filters. Thus, we conclude that our LR model-based KF outperforms standard bandpass filtering techniques in reducing noise, eliminating artifacts, and restoring missing features of saccade signatures present within EOG signals.

A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state

The dataset presented here contains recordings of electroencephalogram (EEG) and electrooculogram (EOG) from four advanced locked-in state (LIS) patients suffering from ALS (amyotrophic lateral sclerosis). These patients could no longer use commercial eye-trackers, but they could still move their eyes and used the remnant oculomotor activity to select letters to form words and sentences using a novel auditory communication system. Data were recorded from four patients during a variable range of visits (from 2 to 10), each visit comprised of 3.22 ± 1.21 days and consisted of 5.57 ± 2.61 sessions recorded per day. The patients performed a succession of different sessions, namely, Training, Feedback, Copy spelling, and Free spelling. The dataset provides an insight into the progression of ALS and presents a valuable opportunity to design and improve assistive and alternative communication technologies and brain-computer interfaces. It might also help redefine the course of progression in ALS, thereby improving clinical judgement and treatment.

Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network

A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen's kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night's polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.

Behavioral-state development and sleep-state differentiation during early ontogenesis

Sleep is a key process in neurodevelopment and essential for the maturation of fundamental brain functions. Premature birth can disturb the initial steps of sleep maturation, which may contribute to the impairment of neurodevelopment. It is thus fundamental to understand the maturation of the various sleep states and the quality of cerebral function in each vigilance state, as well as the development of sleep cyclicity, in at-risk neonatal infants, particularly those born premature. The objective of this review is to provide a precise description of sleep states and cycles and their rhythmic organization in premature and term newborns according to their gestational age. Technical aspects of polysomnography, which requires a high level of expertise in neonates, are also described. Principles of the visual interpretation of polysomnography, including the simultaneous analysis of behavioral (spontaneous motricity and eye movements), polysomnographic parameters (electro-oculogram, electrocardiogram, respiration), and electroencephalography patterns are presented. The neurophysiology of sleep ontogenesis and its interaction with brain maturation are discussed, highlighting the crucial role of sleep states and their duration in premature newborns. In particular, the involvement of myoclonic twitches in functional connectivity in sensorimotor development is discussed. Indeed, sleep quality, determined by combined polysomnographic parameters, reflects either normal or pathological developmental processes during the neonatal period. The fundamental place of neurophysiological explorations in the early detection of sleep disorders is discussed, as well as their potential consequences on neurodevelopmental care to improve the prevention of neurodevelopmental impairment.

Non-contact Measurements of Blink-Associated Electrooculogram Using In-Pillow Cloth Electrodes: Potential Application as a Communication Aid

Paralysis patients, particularly those with amyotrophic lateral sclerosis (ALS), gradually lose the ability to speak because of muscle loss. Even communication through gestures becomes difficult as their condition progresses. Eventually, the only means of communication left is eye movement. Using electrooculogram (EOG) signals, it is possible to improve the communication abilities of those patients who can move their eyes. We examined whether blinking could be detected from the back of the head in a noncontact manner using an in-pillow cloth electrode. We conducted an experiment aimed at detecting blinks in five subjects. The results revealed the possibility of measuring the change of potential related to blinks, with average sensitivity of 96%. This suggested the possibility of establishing a simple tool for ALS patients and paralysis patients to communicate through blinking.

Counteracting effect of verbal ratings of sleepiness on dual task interference

The aim of the present study was to demonstrate the effect of verbal ratings on arousal in the electroencephalogram (EEG) and psychomotor vigilance test (PVT) performance. Thirty participants underwent the PVT for 40 min in three experimental conditions: (1) Rating condition, in which they verbally rated subjective sleepiness with Karolinska sleepiness scale, following pure tone sound played every 20 s during PVT, (2) No-rating condition, in which they underwent PVT with the similar sound as the Rating experiment but without the verbal rating task, and (3) Control condition, in which they underwent PVT with a no-sound stimulus and without the verbal rating task. The results show that during the first half of the task epoch, alpha power density was lower in the Rating than in the No-rating condition, while performance was not different between the conditions. During the second half of the task epoch, performance was better in the Non-rating than in the Rating condition, but no difference in the alpha power density. These results suggest that performance deterioration could be masked by the arousal effect of the dual task itself. It could also explain why the PVT performance and arousal in EEG sometimes dissociate, particularly in dual task situations.

Electrode-free visual prosthesis/exoskeleton control using augmented reality glasses in a first proof-of-technical-concept study

In the field of neuroprosthetics, the current state-of-the-art method involves controlling the prosthesis with electromyography (EMG) or electrooculography/electroencephalography (EOG/EEG). However, these systems are both expensive and time consuming to calibrate, susceptible to interference, and require a lengthy learning phase by the patient. Therefore, it is an open challenge to design more robust systems that are suitable for everyday use and meet the needs of patients. In this paper, we present a new concept of complete visual control for a prosthesis, an exoskeleton or another end effector using augmented reality (AR) glasses presented for the first time in a proof-of-concept study. By using AR glasses equipped with a monocular camera, a marker attached to the prosthesis is tracked. Minimal relative movements of the head with respect to the prosthesis are registered by tracking and used for control. Two possible control mechanisms including visual feedback are presented and implemented for both a motorized hand orthosis and a motorized hand prosthesis. Since the grasping process is mainly controlled by vision, the proposed approach appears to be natural and intuitive.

Reliability analysis of individual visual P1 and N1 maps indicates the heterogeneous topographies involved in early visual processing among human subjects

There is a lack of studies regarding the reliability of the event-related components (ERPs) of an electroencephalogram (EEG) used to assess cognitive processing in human subjects. To explore the reliability scores for the P1 and N1 components in two sessions (separated by an average of 116 days), twenty subjects performed a visual lateralized detection paradigm and EEG recording (58 channels) were employed. The session factor did not modulate the P1/N1 latencies. The visual field factor (left (LVF) or right (RVF)) was a determinant for the P1 and N1 topographical distributions as shown in previous studies. Moreover, topographical maps of the grand average showed a very strong correlation level between sessions (>0.9). Finally, individual maps demonstrated that the classic contralateral pattern for the P1 and N1 components was not always present in all subjects. In particular, compared to the N1 component, the P1 component exhibited a more complex set of individual topographical distributions, revealing that some steps are more heterogeneous among human subjects in early visual processing.

The Clinical Features and Genetic Spectrum of a Large Cohort of Chinese Patients With Vitelliform Macular Dystrophies

PURPOSE

To provide the clinical and genetic characteristics of a large cohort of Chinese patients with vitelliform macular dystrophies.

DESIGN

Cross-sectional study.

METHODS

One hundred and thirty-four unrelated Chinese patients diagnosed with Best vitelliform macular dystrophy (BVMD), autosomal recessive bestrophinopathy (ARB), or adult vitelliform macular dystrophy (AVMD) were enrolled. Detailed ophthalmic examinations and genetic testing on vitelliform macular dystrophy-related genes were performed. Genotype and phenotype association were analyzed among different diagnostic groups.

RESULTS

In total, 87 BVMD, 30 AVMD, and 17 ARB patients were enrolled in this study. Genetic analysis identified 37 BEST1 mutations in 53 patients with BVMD and ARB. Of these, 5 variants (c.254A>C, c.291C>G, c.722C>G, c.848_850del, c.1740-2A>C) were novel. The variant c.898G>A was a hotspot mutation, which was identified in 13 patients with BVMD and 1 patient with ARB. There were significant differences of ocular biometric parameters among patients with homozygous or compound heterozygous mutations, heterozygous mutations, and those without mutations of BEST1. Homozygous or compound heterozygous patients had shortest axial length (AL), shallowest anterior chamber depth (ACD), and highest intraocular pressure (IOP); patients without mutations had longest AL, deepest ACD, and lowest IOP; and heterozygous patients were in between. Moreover, 7 patients harboring heterozygous mutations in BEST1 and 3 patients without BEST1 mutations showed similar clinical appearance to ARB in our cohort.

CONCLUSIONS

This is the largest sample size study of Chinese vitelliform macular dystrophy patients. Our results indicated that assessment of angle-closure risk is a necessary consideration for all types of BEST1-related vitelliform macular dystrophies. The study expanded both the clinical and genetic findings of 3 common types of vitelliform macular dystrophies in a Chinese population.

A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis

Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the ‘cleaned’ samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important.

Dry Electrodes for Human Bioelectrical Signal Monitoring

Bioelectrical or electrophysiological signals generated by living cells or tissues during daily physiological activities are closely related to the state of the body and organ functions, and therefore are widely used in clinical diagnosis, health monitoring, intelligent control and human-computer interaction. Ag/AgCl electrodes with wet conductive gels are widely used to pick up these bioelectrical signals using electrodes and record them in the form of electroencephalograms, electrocardiograms, electromyography, electrooculograms, etc. However, the inconvenience, instability and infection problems resulting from the use of gel with Ag/AgCl wet electrodes can't meet the needs of long-term signal acquisition, especially in wearable applications. Hence, focus has shifted toward the study of dry electrodes that can work without gels or adhesives. In this paper, a retrospective overview of the development of dry electrodes used for monitoring bioelectrical signals is provided, including the sensing principles, material selection, device preparation, and measurement performance. In addition, the challenges regarding the limitations of materials, fabrication technologies and wearable performance of dry electrodes are discussed. Finally, the development obstacles and application advantages of different dry electrodes are analyzed to make a comparison and reveal research directions for future studies.

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.

Temperament and character in primary insomnia

Recent studies by Cloninger suggest that the temperament dimension of harm avoidance might be related to serotonergic activity. Since serotonergic mechanisms equally play a major role in sleep regulation, we decided to use Cloninger’s psychobiological model of temperament and character to assess whether there is a link between psychophysiologic insomnia and specific personality traits. Chronic insomnia is a common complaint in modern society, and it is still controversial whether insomniacs share specific personality traits. Thirty-two chronic insomniacs (<50 years) were studied. They underwent polysomnography for two consecutive nights and filled out the 226-item self-questionnaire of Temperament and Character Inventory as well as the Hospital Anxiety and Depression scale. (1) Harm avoidance for all subscores was significantly higher in insomniac patients when compared with controls; (2) self-directedness scores were lower in insomniacs; (3) sleep latency was positively correlated to harm avoidance; (4) HA1 (anticipatory worry) was negatively correlated to REM latency. Temperament and Character Inventory is a useful tool in the investigation of chronic insomnia. Serotonergic mechanisms might explain the high incidence of harm avoidance as personality trait in psychophysiologic insomniac patients. Further studies are needed to see whether harm avoidance could be a psychological vulnerability marker for primary insomnia and be used as predictor of SSRI treatment responders.

A unique PRDM13-associated variant in a Georgian Jewish family with probable North Carolina macular dystrophy and the possible contribution of a unique CFH variant

Purpose

North Carolina macular dystrophy (NCMD) is an autosomal dominant maculopathy that is considered a non-progressive developmental disorder with variable expressivity. Our study aimed to clinically and genetically characterize macular dystrophy in a family (MOL1154) consisting of six affected subjects with a highly variable maculopathy phenotype in which no correlation between age and severity exists.

Methods

Clinical characterization included visual acuity testing and electroretinography. Genetic analysis included Sanger sequencing and whole exome sequencing (WES).

Results

WES analysis performed on DNA samples from two individuals revealed a heterozygous deletion of six nucleotides [c.2247_2252del; p.(Leu750_Lys751del)] in the CFH gene. Co-segregation analysis revealed that five of the six NCMD affected subjects carried this deletion, while one individual who had a relatively mild phenotype compatible with dry age-related macular degeneration (AMD) did not carry it. We subsequently analyzed the upstream region of PRDM13 that has previously been reported to be associated with NCMD and identified a unique heterozygous transversion (chr6:100040974A>C) located within the previously described suspected control region in all six affected individuals. This transversion is likely to cause NCMD.

Conclusions

NCMD has a wide spectrum of clinical phenotypes that can overlap with AMD, making it challenging to correctly diagnose affected individuals and family members. The DNA sequence variant we found in the CFH gene of some of the affected family members may suggest some role as a modifier gene. However, this variant still does not explain the huge phenotypic variability of NCMD and needs to be studied in other and larger populations.

A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals

OBJECTIVE

A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals.

METHODS

Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results.

RESULTS

Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%.

CONCLUSION

The proposed BCI generates multiple commands with a high ITR and low FPR.

SIGNIFICANCE

The hybrid asynchronous BCI has great potential for practical applications in communication and control.

A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking

We present a new wearable electrooculogram (EOG) monitor for measuring eye movements. We fabricated conductive and flexible graphene-based textiles from nylon to use as a sensing electrode, which we then integrated into a commercially available eye mask held in place only with the standard elastic strap. We tested this mask on 4 participants to quantify the noise floor and show that we can detect eye blinks to a high SNR of over 16 dB. We also identify that the material can detect other eye movements in cases when the noise floor is low. As our system is held in place with only an elastic strap it offers the same level of comfort as when wearing a normal eye mask. Our sensors offer an increased level of comfort over conventional gelled electrodes traditionally used in EOG monitoring and may be of use for comfortable eye movement experiments. This is particularly important during sleep studies where the EOG is routinely monitored, but using bulky instrumentation.

Clinical and Genetic Findings of Autosomal Recessive Bestrophinopathy (ARB)

Mutations in BEST1 cause several phenotypes including autosomal dominant (AD) Best vitelliform macular dystrophy type 2 (BVMD), AD vitreo-retino-choroidopathy (ADVIRC), and retinitis pigmentosa-50 (RP50). A rare subtype of Bestrophinopathy exists with biallelic mutations in BEST1. Its frequency is estimated to be 1/1,000,000 individuals. Here we report 6 families and searched for a genotype-phenotype correlation. All patients were referred due to reduced best-corrected visual acuity (BCVA), ranging from 0.1/10 to 3/10. They all showed vitelliform lesions located at the macula, sometimes extending into the midperiphery, along the vessels and the optic disc. Onset of the disease varied from the age of 3 to 25 years. Electrooculogram (EOG) revealed reduction in the EOG light rise in all patients. Molecular analysis revealed previously reported mutations p.(E35K);(E35K), p.(L31M);(L31M), p.(R141H);(A195V), p.(R202W);(R202W), and p.(Q220*);(Q220*) in five families. One family showed a novel mutation: p.(E167G);(E167G). All mutations were heterozygous in the parents. In one family, heterozygous children showed various reductions in the EOG light rise and autofluorescent deposits. Autosomal recessive Bestrophinopathy (ARB), although rare, can be recognized by its phenotype and should be validated by molecular analysis. Genotype-phenotype correlations are difficult to establish and will require the analysis of additional cases.

A New Fully Automated Random-Forest Algorithm for Sleep Staging

Rapid eye movement (REM) sleep behavior disorder is considered the prodromal stage of alpha-synucleinopathies. Its diagnosis requires careful detection of REM sleep and the gold standard manual sleep staging is inconsistent and expensive. This work proposes a new automatic sleep staging model to add robust automation to such applications, using only electroencephalography (EEG) and electrooculography (EOG) recordings. The publicly available ISRUC-Sleep database was used to optimize the design of the proposed model. The model was trained and tested on subgroup-I consisting of 100 subjects with evidence of having different sleep disorders and the polysomnographic data were manually scored by two individual experts. We divided the EOG and EEG recordings in overlapping moving 33-s epochs with step of 3s and for each of them we computed several time and frequency-domain features. The features were used to train a random forest classifier that was able to label each 33-s epoch with the probabilities of being wakefulness, REM and non-REM. The mean of the probability values of ten 33-s epochs were calculated, and the sleep stage with the highest probability was chosen to classify a 30-s epoch and matched with the manual staged hypnogram. The performance of the model was tested using 20-fold cross validation scheme. When the epochs where the scorers agreed were used, the classification achieved an overall accuracy of 92.6% and a Cohen's kappa of 0.856. Future validation on RBD patients is needed, but these performances are promising as first step of development of an automated diagnosis of RBD.

Cerebellum-mediated trainability of eye and head movements for dynamic gazing

Objective

To investigate whether gaze stabilization exercises (GSEs) improve eye and head movements and whether low-frequency cerebellar repetitive transcranial magnetic stimulation (rTMS) inhibits GSE trainability.

Methods

25 healthy adults (real rTMS, n = 12; sham rTMS, n = 13) were recruited. Real or sham rTMS was performed for 15 min (1 Hz, 900 stimulations). The center of the butterfly coil was set 1 cm below the inion in the real rTMS. Following stimulation, 10 trials of 1 min of a GSE were conducted at 1 min intervals. In the GSE, the subjects were instructed to stand upright and horizontally rotate their heads according to a beeping sound corresponding to 2 Hz and with a gaze point ahead of them. Electrooculograms were used to estimate the horizontal gaze direction of the right eye, and gyroscopic measurements were performed to estimate the horizontal head angular velocity during the GSE trials. The percentage change from the first trial of motion range of the eye and head was calculated for each measurement. The percent change of the eye/head range ratio was calculated to assess the synchronous changes of the eye and head movements as the exercise increased.

Results

Bayesian two-way analysis of variance showed that cerebellar rTMS affected the eye motion range and eye/head range ratio. A post hoc comparison (Bayesian t-test) showed evidence that the eye motion range and eye/head range ratio were reduced in the fifth, sixth, and seventh trials compared with the first trial sham stimulation condition.

Conclusions

GSEs can modulate eye movements with respect to head movements, and the cerebellum may be associated with eye–head coordination trainability for dynamic gazing during head movements.

Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

Unsupervised Common Spatial Patterns

The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto directions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurtosis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.

Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

Current sleep medicine relies on the supervised analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep based on these raw waveforms. In this study, we compare existing CNN approaches to four databases of pathological and physiological subjects. The best performing model resulted in Cohen's Kappa of $\kappa = 0 .75$ on healthy subjects and $\kappa = 0 .64$ on patients suffering from a variety of sleep disorders. Further, we show the advantages of additional sensor data (i.e., EOG and EMG). Deep learning approaches require a lot of data which is scarce for less prevalent diseases. For this, we propose a transfer learning procedure by pretraining a model on large public data and fine-tune this on each subject from a smaller dataset. This procedure is demonstrated using a private REM Behaviour Disorder database, improving sleep classification by 24.4%.

Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds

BACKGROUND AND OBJECTIVE

The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polysomnography (PSG) recording of patients, wherein electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) play the dominant role. Developing an automatic sleep staging system based on multi-channel physiological signals could relieve the burden of manual labeling by experts, and obtain reliable and repeatable recognition results as well.

METHODS

In this work, we find the correlation between the spatial covariance matrices of multi-channel signals and their corresponding sleep stages. Based on that, we propose two novel sleep stage classification methods based on the features extracted from the covariance matrices of multi-channel signals. Sleep stages are classified using a minimum distance classifier according to their corresponding covariance matrices mapped on Riemannian manifolds. An alternative way to classify these covariance matrices is to represent the features of covariance matrices on the tangent space of Riemannian manifolds and classify them with an ensemble learning classifier. After any of these classification methods, a rule-free refinement process is utilized to further optimize the classification results.

RESULTS

On the MASS dataset that includes 61 whole-night PSG recordings, both two methods provide satisfactory classification results while the one based on tangent space projection has better performance. On average, an accuracy of 0.812 and a Cohen's Kappa coefficient of 0.722 are obtained under leave-one-subject-out cross validation, using EEG, EOG and EMG signals. Meanwhile, the most effective combinations of EEG channels for sleep staging have been found in this work.

CONCLUSIONS

The correlation between spatial covariance matrices of multi-channel signals and their corresponding sleep stages have been found. Features based on that are used for sleep stage classification, and experimental results show the superior performance of proposed methods compared to state-of-the-art works. Results of this work are expected to provide a new vision for dealing with multi-channel or multi-modal signal processing tasks in various applications.