High-performance and low-power source-gated transistors enabled by a solution-processed metal oxide homojunction.

Cost-effective fabrication of mechanically flexible low-power electronics is important for emerging applications including wearable electronics, artificial intelligence, and the Internet of Things. Here, solution-processed source-gated transistors (SGTs) with an unprecedented intrinsic gain of ~2,000, low saturation voltage of +0.8 ± 0.1 V, and a ~25.6 µW power consumption are realized using an indium oxide In2O3/In2O3:polyethylenimine (PEI) blend homojunction with Au contacts on Si/SiO2. Kelvin probe force microscopy confirms source-controlled operation of the SGT and reveals that PEI doping leads to more effective depletion of the reverse-biased Schottky contact source region. Furthermore, using a fluoride-doped AlOx gate dielectric, rigid (on a Si substrate) and flexible (on a polyimide substrate) SGTs were fabricated. These devices exhibit a low driving voltage of +2 V and power consumption of ~11.5 µW, yielding inverters with an outstanding voltage gain of >5,000. Furthermore, electrooculographic (EOG) signal monitoring can now be demonstrated using an SGT inverter, where a ~1.0 mV EOG signal is amplified to over 300 mV, indicating significant potential for applications in wearable medical sensing and human-computer interfacing.

Extreme olfactory sensitivity of silver and bighead carp to overlapping suites of 21-carbon steroids suggests that these species, and likely all other Cyprinoidei, employ them as pheromones.

Although well established that several fishes including goldfish in the suborder Cypinoidei within the family Cypriniformes use the maturation-inducing steroid 17,20ß-dihydroxy-pregn-4-ene-3-one (17,20ßP) and its metabolites as a priming pheromone which they detect with sensitivity and specificity, it is unclear whether and how other Cypriniformes might have evolved to do so. This study examined this question in the family Xenocyprididae. Using electro-olfactogram recording we tested the olfactory sensitivity of silver (Hypophthalmichthys molitrix) and bighead carp (H. nobilis) to a range of 213 steroids in 21 mixtures at 10-9M. While silver carp detected 6 of 21 mixtures, bighead carp detected 5 (p< 0.05). Silver carp were sensitive to 13 21-carbon steroids in these mixtures including 17,20ßP while bighead carp detected 9, including 8 detected by silver carp. This assortment of steroids overlapped that detected by goldfish (family Cyprinidae) but no non-Cyprinoid, suggesting common evolutionary origin and function with differences characteristic of species-specificity.

Flexible complementary circuits operating at sub-0.5 V via hybrid organic-inorganic electrolyte-gated transistors.

Electrolyte-gated transistors (EGTs) hold great promise for next-generation printed logic circuitry, biocompatible integrated sensors, and neuromorphic devices. However, EGT-based complementary circuits with high voltage gain and ultralow driving voltage (<0.5 V) are currently unrealized, because achieving balanced electrical output for both the p- and n-type EGT components has not been possible with current materials. Here we report high-performance EGT complementary circuits containing p-type organic electrochemical transistors (OECTs) fabricated with an ion-permeable organic semiconducting polymer (DPP-g2T) and an n-type electrical double-layer transistor (EDLT) fabricated with an ion-impermeable inorganic indium-gallium-zinc oxide (IGZO) semiconductor. Adjusting the IGZO composition enables tunable EDLT output which, for In:Ga:Zn = 10:1:1 at%, balances that of the DPP-g2T OECT. The resulting hybrid electrolyte-gated inverter (HCIN) achieves ultrahigh voltage gains (>110) under a supply voltage of only 0.7 V. Furthermore, NAND and NOR logic circuits on both rigid and flexible substrates are realized, enabling not only excellent logic response with driving voltages as low as 0.2 V but also impressive mechanical flexibility down to 1-mm bending radii. Finally, the HCIN was applied in electrooculographic (EOG) signal monitoring for recording eye movement, which is critical for the development of wearable medical sensors and also interfaces for human-computer interaction; the high voltage amplification of the present HCIN enables EOG signal amplification and monitoring in which a small ∼1.5 mV signal is amplified to ∼30 mV.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

ISCEV guidelines for calibration and verification of stimuli and recording instruments (2023 update).

This document developed by the International Society for Clinical Electrophysiology of Vision (ISCEV) provides guidance for calibration and verification of stimulus and recording systems specific to clinical electrophysiology of vision. This guideline provides additional information for those using ISCEV Standards and Extended protocols and supersedes earlier Guidelines. The ISCEV guidelines for calibration and verification of stimuli and recording instruments (2023 update) were approved by the ISCEV Board of Directors 01, March 2023.

EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.

The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements; left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz; training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index.

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.

Continuous Biopotential Monitoring via Carbon Nanotubes Paper Composites (CPC) for Sustainable Health Analysis.

Skin-based wearable devices have gained significant attention due to advancements in soft materials and thin-film technologies. Nevertheless, traditional wearable electronics often entail expensive and intricate manufacturing processes and rely on metal-based substrates that are susceptible to corrosion and lack flexibility. In response to these challenges, this paper has emerged with an alternative substrate for wearable electrodes due to its cost-effectiveness and scalability in manufacturing. Paper-based electrodes offer an attractive solution with their inherent properties of high breathability, flexibility, biocompatibility, and tunability. In this study, we introduce carbon nanotube-based paper composites (CPC) electrodes designed for the continuous detection of biopotential signals, such as electrooculography (EOG), electrocardiogram (ECG), and electroencephalogram (EEG). To prevent direct skin contact with carbon nanotubes, we apply various packaging materials, including polydimethylsiloxane (PDMS), Eco-flex, polyimide (PI), and polyurethane (PU). We conduct a comparative analysis of their signal-to-noise ratios in comparison to conventional gel electrodes. Our system demonstrates real-time biopotential monitoring for continuous health tracking, utilizing CPC in conjunction with a portable data acquisition system. The collected data are analyzed to provide accurate heart rates, respiratory rates, and heart rate variability metrics. Additionally, we explore the feasibility using CPC for sleep monitoring by collecting EEG signals.

A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.

Coupling brain-computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one's brain activity only. These types of systems might be of a particular interest for people with locked-in syndrome (LIS) or amyotrophic lateral sclerosis (ALS) because they can benefit from communicating with robotic assistants using brain sensing interfaces. In this proof-of-concept work, we explored how a wireless and wearable BCI device can control a quadruped robot-Boston Dynamics' Spot. The device measures the user's electroencephalography (EEG) and electrooculography (EOG) activity of the user from the electrodes embedded in the glasses' frame. The user responds to a series of questions with YES/NO answers by performing a brain-teaser activity of mental calculus. Each question-answer pair has a pre-configured set of actions for Spot. For instance, Spot was prompted to walk across a room, pick up an object, and retrieve it for the user (i.e., bring a bottle of water) when a sequence resolved to a YES response. Our system achieved at a success rate of 83.4%. To the best of our knowledge, this is the first integration of wireless, non-visual-based BCI systems with Spot in the context of personal assistant use cases. While this BCI quadruped robot system is an early prototype, future iterations may embody friendly and intuitive cues similar to regular service dogs. As such, this project aims to pave a path towards future developments in modern day personal assistant robots powered by wireless and wearable BCI systems in everyday living conditions.

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.

Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection.

Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.

Tolerability and safety of a new elimination diet for pediatric eosinophilic gastritis and duodenitis.

BACKGROUND: Non-esophageal eosinophilic gastrointestinal disorders (non-EoE EGIDs) are chronic inflammatory disorders with massive infiltration of eosinophils into the gastrointestinal tract. Food elimination diets are potentially effective treatments. But the existing dietary therapies have various weak points. We developed a new regimen to compensate for the shortcomings of the elemental diet and 6-food elimination diet. The new regimen consists of an amino-acid-based formula, potatoes, vegetables, fruits and restricted seasonings. We named it the "Rainbow Elimination Diet (ED)." The aims of this study were to evaluate the tolerability and safety of this diet. METHODS: A retrospective medical record examination was conducted at the National Center for Child Health and Development covering the period from January 2010 through December 2018. The medical records of patients (age 2-17 y) with histologically diagnosed non-EoE EGIDs were reviewed. The tolerability, nutritional intake, symptoms, and blood test findings were evaluated. RESULTS: Nineteen patients were offered several kinds of food-elimination diets. Seven patients (eosinophilic gastritis: 5; gastroenteritis: 1; duodenitis: 1) were treated with Rainbow ED. Six patients were compliant with this diet. The median duration of the diet induction phase was 15 days (range 14-30). All 5 patients who had had symptoms just before the induction phase became symptom-free. The body weight decreased in 5 patients (median -0.6 kg), probably because the serum protein increased, resulting in reduced edema. All 5 patients with hypoproteinemia had elevated serum albumin (median 2.9-3.5 g/dL). The ingested nutritional elements were calculated, and most of them were sufficient, except for fat and selenium. CONCLUSIONS: The Rainbow ED was well-tolerated and safe for pediatric non-EoE EGIDs.

Timing and kinematics of horizontal within-blink saccades measured by EOG.

Eyeblinks are the brief closures of the lid. They are accompanied by a cocontraction of the eye muscles that temporarily pulls the whole eyeball back into its socket. When blinks occur together with execution of saccadic gaze shifts, they interfere with the saccadic premotor circuit, causing these within-blink saccades to be slower than normal and also time-locked to blinks. To analyze the trajectory of within-blink saccades, subtraction of the entangled blink-related eye movement is required. Here we propose a combination of principal component analysis (PCA) and a regression model to subtract the blink-related component of the eye movement based on the respective blink metrics. We used electrooculography (EOG) to measure eye and lid movements of 12 participants who performed saccades with and without blinks. We found that within-blink saccades are slower than without-blink saccades and are tightly coupled in time to blink onset. Surprisingly, in some participants we observed large dynamic overshoots of up to 15° for saccades of only 5° amplitude. The finding of dynamic overshoots was independently confirmed by dynamic MRI for two of the participants and challenges the current view that within-blink saccades are programmed as slow, but straight, saccades. We hypothesize that the dynamic overshoots could be attributed to inhibition of omnipause neurons during blinks, the simultaneous cocontraction of extraocular muscles, or a combination of both.NEW & NOTEWORTHY This study observed that people make large dynamic overshoots when making a saccadic eye movement within a blink but their eyes are back on target by the time the eyelids are open. We used electrooculography (EOG) to measure eye movements even when the lid is down and introduced a novel procedure to subtract blink-related EOG components. These findings challenge the current view that within-blink saccades are programmed as slow but straight saccades.

Intranasal delivery of SARS-CoV-2 spike protein is sufficient to cause olfactory damage, inflammation and olfactory dysfunction in zebrafish.

Anosmia, loss of smell, is a prevalent symptom of SARS-CoV-2 infection. Anosmia may be explained by several mechanisms driven by infection of non-neuronal cells and damage in the nasal epithelium rather than direct infection of olfactory sensory neurons (OSNs). Previously, we showed that viral proteins are sufficient to cause neuroimmune responses in the teleost olfactory organ (OO). We hypothesize that SARS-CoV-2 spike (S) protein is sufficient to cause olfactory damage and olfactory dysfunction. Using an adult zebrafish model, we report that intranasally delivered SARS-CoV-2 S RBD mostly binds to the non-sensory epithelium of the olfactory organ and causes severe olfactory histopathology characterized by loss of cilia, hemorrhages and edema. Electrophysiological recordings reveal impaired olfactory function to both food and bile odorants in animals treated intranasally with SARS-CoV-2 S RBD. However, no loss of behavioral preference for food was detected in SARS-CoV-2 S RBD treated fish. Single cell RNA-Seq of the adult zebrafish olfactory organ indicated widespread loss of olfactory receptor expression and inflammatory responses in sustentacular, endothelial, and myeloid cell clusters along with reduced numbers of Tregs. Combined, our results demonstrate that intranasal SARS-CoV-2 S RBD is sufficient to cause structural and functional damage to the zebrafish olfactory system. These findings may have implications for intranasally delivered vaccines against SARS-CoV-2.

Olfactory adaptation: recordings from the human olfactory epithelium.

PURPOSE: Olfactory adaptation is a peripheral (at the epithelium level) or a central (at the brain level) mechanism resulting from repeated or prolonged odorous exposure that can induce a perceptual decrease. The aim of this study was to assess whether a peripheral adaptation occurs when an odor is repeated ten times. Moreover, the specificity of the peripheral adaptation to the nature of the odorant was investigated. METHODS: Four odorants (eugenol, manzanate, ISO E Super and phenylethanol) were presented using precisely controlled air-dilution olfactometry. They differed in terms of their physicochemical properties. Electrophysiological recordings were made at the level of the olfactory mucosa, the so-called electro-olfactogram (EOG). Thirty-five right-handed participants were recruited. RESULTS: Sixty-nine percent of the participants presented at least one EOG, whatever the odor condition. The EOG amplitude did not significantly decrease over 10 repeated exposures to any odorant. The intensity ratings tended to decrease over stimulations for manzanate, PEA, and eugenol. No correlation was found between the mean EOG amplitudes and the mean intensity ratings. However, the presence of EOG amplitude decreases over stimulations for few subjects suggests that peripheral adaptation might exist. CONCLUSION: Overall, our results did not establish a clear peripheral adaptation measured with EOG but indicate the eventuality of such an effect.

EOG-Based Human-Computer Interface: 2000-2020 Review.

Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals.

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

Olfactory Deprivation and Enrichment: An Identity of Opposites?

The effects of deprivation and enrichment on the electroolfactogram of mice were studied through the paradigms of unilateral naris occlusion and odor induction, respectively. Deprivation was shown to cause an increase in electroolfactogram amplitudes after 7 days. We also show that unilateral naris occlusion is not detrimental to the gross anatomical appearance or electroolfactogram of either the ipsilateral or contralateral olfactory epithelium even after year-long survival periods, consistent with our previous assumptions. Turning to induction, the increase in olfactory responses after a period of odor enrichment, could not be shown in CD-1 outbred mice for any odorant tried. However, consistent with classical studies, it was evident in C57BL/6J inbred mice, which are initially insensitive to isovaleric acid. As is the case for deprivation, enriching C57BL/6J mice with isovaleric acid causes an increase in their electroolfactogram response to this odorant over time. In several experiments on C57BL/6J mice, the odorant specificity, onset timing, recovery timing, and magnitude of the induction effect were studied. Considered together, the current findings and previous work from the laboratory support the counterintuitive conclusion that both compensatory plasticity in response to deprivation and induction in response to odor enrichment are caused by the same underlying homeostatic mechanism, the purpose of which is to preserve sensory information flow no matter the odorant milieu. This hypothesis, the detailed evidence supporting it, and speculations concerning human odor induction are discussed.

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.

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.