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Towards canine brain–computer interfaces (BCIs)
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Sine-wave f0 fMRI Dog Human a b s t r a c t Voice-sensitivity in the auditory cortex of a range of mammals has been proposed to be determined primarily by tuning to conspecific auditory stimuli, but recent human findings indicate a role for a more general tuning to voicelikeness. Vocal emotional valence, a central characteristic of vocalisations, has been linked to the same basic acoustic parameters across species. Comparative neuroimaging revealed that during voice perception, such acoustic parameters modulate emotional valence-sensitivity in auditory cortical regions in both family dogs and humans. To explore the role of voicelikeness in auditory emotional valence-sensitivity across species, here we constructed artificial emotional sounds in two sound categories: voice-like vs. sine-wave sounds, parametrically modulating two main acoustic parameters, f0 and call length. We hypothesised that if mammalian auditory systems are characterised by a general tuning to voicelikeness, voice-like sounds will be processed preferentially, and acoustic parameters for voice-like sounds will be processed differently than for sine-wave sounds-both in dogs and humans. We found cortical areas in both species that responded stronger to voice-like than to sine-wave stimuli, while there were no regions responding stronger to sine-wave sounds in either species. Additionally, we found that in bilateral primary and emotional valence-sensitive auditory regions of both species, the processing of voice-like and sine-wave sounds are modulated by f0 in opposite ways. These results reveal functional similarities between evolutionarily distant mammals for processing voicelikeness and its effect on processing basic acoustic cues of vocal emotions.
Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of n = 17 family dogs to human and conspecific emotional vocalizations using a fully non-invasive event-related potential (ERP) paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller, demonstrated by a more positive ERP response to human vocalizations compared to dog vocalizations in a time window between 250 and 650 ms after stimulus onset. A later time window between 800 and 900 ms also revealed a valence-sensitive ERP response in interaction with the species of the caller. Our results are, to our knowledge, the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time periods.
The sleeping activity of family dogs has been studied increasingly in the past years. Recently, a validated, non-invasive polysomnographic method has been developed for dogs, enabling the parallel recording of several neurophysiological signals on non-anesthetized family dogs, including brain activity (EEG), eye movements (EOG), cardiac (ECG), and respiratory activity (PNG). In this study, we examined the ECG (N = 30) and respiratory signals (N = 19) of dogs during a 3-h sleep period in the afternoon, under laboratory conditions. We calculated four time-domain heart rate variables [mean heart rate (HR), SDNN, RMSSD, and pNN50] from the ECG and the estimated average respiratory frequency from the respiratory signal. We analyzed how these variables are affected by the different sleep-wake phases (wakefulness, drowsiness, NREM, and REM) as well as the dogs' sex, age and weight. We have found that the sleep-wake phase had a significant effect on all measured cardiac parameters. In the wake phase, the mean HR was higher than in all other phases, while SDNN, RMSSD, and pNN50 were lower than in all other sleep phases. In drowsiness, mean HR was higher compared to NREM and REM phases, while SDNN and RMSSD was lower compared to NREM and REM phases. In REM, SDNN, and RMSSD was higher than in NREM. However, the sleep-wake phase had no effect on the estimated average respiratory frequency of dogs. The dogs' sex, age and weight had no effect on any of the investigated variables. This study represents a detailed analysis of the cardiac and respiratory activity of dogs during sleep. Since variations in these physiological signals reflect the dynamics of autonomic functions, a more detailed understanding of their changes may help us to gain a better understanding of the internal/emotional processes of dogs in response to different conditions of external stimuli. As such, our results are important since they are directly comparable to human findings and may also serve as a potential basis for future studies on dogs.
A bstract
The emerging field of canine cognitive neuroscience uses neuroimaging tools such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to map the cognitive processes of dogs to neural substrates in their brain. Within the past decade, the non-invasive use of EEG has provided real-time, accessible, and portable neuroimaging insight into canine cognitive processes. To promote systematization and create an overview of framings, methods and findings for future work, we provide a systematic review of non-invasive canine EEG studies (N=22), dissecting their study makeup, technical setup, and analysis frameworks and highlighting emerging trends. We further propose new directions of development, such as the standardization of data structures and integrating predictive modeling with descriptive statistical approaches. Our review ends by underscoring the advances and advantages of EEG-based canine cognitive neuroscience and the potential for accessible canine neuroimaging to inform both fundamental sciences as well as practical applications for cognitive neuroscience, working dogs, and human-canine interactions.
Drug detection dogs play integral roles in society. However, the interplay between their behaviors and genetic characteristics underlying their performance remains uninvestigated. Herein, more than 120,000 genetic variants were evaluated in 326 German Shepherd or Labrador Retriever dogs to profile the genetic traits associated with various behavioral traits related to the successful training of drug detection dogs. Behavioral breed differences were observed in ‘friendliness to humans’ and ‘tolerance to dogs.’ A genome-wide association study within both breeds identified 11 regions potentially associated with drug detection dog characteristics as well as ‘interest in the target’ and ‘friendliness to humans,’ which are related to drug detection abilities. Among them, 63 protein-coding genes, including Atat1 and Pfn2 known to be associated with anxiety-related or exploration behavior in mice, respectively, were located surrounding the identified candidate polymorphisms. This study highlights genetic characteristics associated with behavioral traits that are important for the successful training of drug detection dogs. Thus, these findings may facilitate improved breeding and training of these dogs.
Comparative neuroimaging allows for the identification of similarities and differences between species. It provides an important and promising avenue, to answer questions about the evolutionary origins of the brain´s organization, in terms of both structure and function. Dog functional magnetic resonance imaging (fMRI) has recently become one particularly promising and increasingly used approach to study brain function and coevolution. In dog neuroimaging, image acquisition has so far been mostly performed with coils originally developed for use in human MRI. Since such coils have been tailored to human anatomy, their sensitivity and data quality is likely not optimal for dog MRI. Therefore, we developed a multichannel receive coil (K9 coil, read “canine”) tailored for high-resolution functional imaging in canines, optimized for dog cranial anatomy. In this paper we report structural ( n = 9) as well as functional imaging data (resting-state, n = 6; simple visual paradigm, n = 9) collected with the K9 coil in comparison to reference data collected with a human knee coil. Our results show that the K9 coil significantly outperforms the human knee coil, improving the signal-to-noise ratio (SNR) across the imaging modalities. We noted increases of roughly 45% signal-to-noise in the structural and functional domain. In terms of translation to fMRI data collected in a visual flickering checkerboard paradigm, group-level analyses show that the K9 coil performs better than the knee coil as well. These findings demonstrate how hardware improvements may be instrumental in driving data quality, and thus, quality of imaging results, for dog-human comparative neuroimaging.
Subjective sleep disturbances are reported by humans with attention-deficit/hyperactivity disorder (ADHD). However, no consistent objective findings related to sleep disturbances led to the removal of sleep problems from ADHD diagnostic criteria. Dogs have been used as a model for human ADHD with questionnaires validated for this purpose. Also, their sleep physiology can be measured by non-invasive methods similarly to humans. In the current study, we recorded spontaneous sleep EEG in family dogs during a laboratory session. We analyzed the association of sleep macrostructure and deep sleep (NREM) slow-wave activity (SWA) with a validated owner-rated ADHD questionnaire, assessing inattention (IA), hyperactivity/impulsivity (H/I) and total (T) scores. Higher H/I and T were associated with lower sleep efficiency and longer time awake after initial drowsiness and NREM. IA showed no associations with sleep variables. Further, no association was found between ADHD scores and SWA. Our results are in line with human studies in which poor sleep quality reported by ADHD subjects is associated with some objective EEG macrostructural parameters. This suggests that natural variation in dogs’ H/I is useful to gain a deeper insight of ADHD neural mechanisms.
Liquid metal-elastomer composite is a promising soft conductor for skin-interfaced bioelectronics, soft robots, and others due to its large stretchability, ultrasoftness, high electrical conductivity, and mechanical-electrical decoupling. However, it often suffers from deformation-induced leakage, which can smear skin, deteriorate device performance, and cause circuit shorting. Besides, antimicrobial property is desirable in soft conductors to minimize microbial infections. Here, we report phase separation-based synthesis of porous liquid metal-elastomer composites with high leakage resistance and antimicrobial property, together with large stretchability, tissue-like compliance, high and stable electrical conductivity over deformation, high breathability, and magnetic resonance imaging compatibility. The porous structures can minimize leakage through damping effects and lower percolation thresholds to reduce liquid metal usage. In addition, epsilon polylysine is loaded into elastic matrices during phase separation to provide antimicrobial property. The enabled skin-interfaced bioelectronics can monitor cardiac electrical and mechanical activities and offer electrical stimulations in a mechanically imperceptible and electrically stable manner even during motions.
In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs’ facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network’s attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye.
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturisation. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 44 wireless EEG devices along with a number of important—sometimes difficult-to-obtain—features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorised by application and analysed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary with respect to pitfalls and caveats regarding this increasingly accessible line of research.
Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.
The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.
There are approximately a half million active service dogs in the United States, providing life-changing assistance and independence to people with a wide range of disabilities. The tremendous value of service dogs creates significant demand, which service dog providers struggle to meet. Breeding, raising, and training service dogs is an expensive, time-consuming endeavor which is exacerbated by expending resources on dogs who ultimately will prove to be unsuitable for service dog work because of temperament issues. Quantifying behavior and temperament through sensor-instrumented dog toys can provide a way to predict which dogs will be suitable for service dog work, allowing resources to be focused on the dogs likely to succeed. In a 2-year study, we tested dogs in advanced training at Canine Companions for Independence with instrumented toys, and we discovered that a measure of average bite duration is significantly correlated with a dog's placement success as a service dog [Adjusted OR = 0.12, Pr(>|z|) = 0.00666]. Applying instrumented toy interactions to current behavioral assessments could yield more accurate measures for predicting successful placement of service dogs while reducing the workload of the trainers.
Ataxia is an impairment of the coordination of movement or the interaction of associated muscles, accompanied by a disturbance of the gait pattern. Diagnosis of this clinical sign, and evaluation of its severity is usually done using subjective scales during neurological examination. In this exploratory study we investigated if inertial sensors in a smart phone (3 axes of accelerometer and 3 axes of gyroscope) can be used to detect ataxia. The setting involved inertial sensor data collected by smartphone placed on the dog's back while walking in a straight line. A total of 770 walking sessions were evaluated comparing the gait of 55 healthy dogs to the one of 23 dogs with ataxia. Different machine learning techniques were used with the K-nearest neighbors technique reaching 95% accuracy in discriminating between a healthy control group and ataxic dogs, indicating potential use for smartphone apps for canine ataxia diagnosis and monitoring of treatment effect.
Electrophysiology signals are crucial health status indicators as they are related to all human activities. Current demands for mobile healthcare have driven considerable interest in developing skin-mounted electrodes for health monitoring. Silver-Silver chloride-based (Ag-/AgCl) wet electrodes, commonly used in conventional clinical practice, provide excellent signal quality, but cannot monitor long-term signals due to gel evaporation and skin irritation. Therefore, the focus has shifted to developing dry electrodes that can operate without gels and extra adhesives. Compared to conventional wet electrodes, dry ones offer various advantages in terms of ease of use, long-term stability, and biocompatibility. This review outlines a systematic summary of the latest research on high-performance soft and dry electrodes. In addition, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, strategies to promote physical adhesion, methods for higher breathability, and their applications in wearable biomedical devices. Finally, we discuss the developmental challenges and advantages of various dry electrodes, while suggesting research directions for future studies.
Physiological measurements of dogs' emotional states during human-animal interactions are essential for understanding the underlying biological relationship. Heart rate measured by electrocardiogram (ECG) can be used for the physiological measurement of emotional state. Soft disposable electrodes, which can be purchased commercially and reduce the risk of an injury on hitting objects, are used to measure the heart rate during working dogs' exercise. However, the heart rate measurements using soft disposable electrodes in M-X lead layout are unstable due to the detachment of electrodes during intensive movement. In addition, the collection of accurately labeled ECG data, which reflects the emotion of the working dog during exercise, is a key issue to conduct reliable estimation and visualization of the working dog's emotional states. In this paper, we propose an innerwear that fixes soft disposable electrodes in M-X lead layout on a dog's body for measuring the heart rates of exercising dogs, even during intense movement. We experimentally tested the proposed method using seven dogs. Our proposed system reduced the time required to attach soft disposable electrodes by more than two-thirds and enabled the measurement of >97% of the heartbeats in exercising dogs. We also proposed a method for collecting ECG data with reliable positive/negative emotional labels of search and rescue (SAR) dogs. A combination of the collected data with the emotion estimation algorithm proposed in previous studies estimated SAR dogs' positive/negative emotions at 97.9% accuracy. The transition of the two SAR dogs' emotions during drills was visualized using the collected data.
Simple Summary
Dogs have been shown to form attachment bonds towards their owners analogous to the human infant-parent attachment. In humans, the neurological background of variation in attachment and similar trait-like social behaviors has been described. It is known that certain sleep parameters are in association with an individual’s attachment-related traits. In the current study, we provide the first evidence that dogs’ attachment towards their owner is also associated to dogs’ sleep structure (the time they spend in the different sleep stages) as well as to their brain activity during sleep. Thus, as in humans, when dogs sleep in a novel environment (in the presence of their owners), differences in their attachment bond are reflected in their sleep EEG characteristics.
Abstract
Affective neuroscience studies have demonstrated the impact of social interactions on sleep quality. In humans, trait-like social behaviors, such as attachment, are related to sleep brain activity patterns. Our aim was to investigate associations between companion dogs’ spontaneous brain activity during sleep (in the presence of the owner) and their relevant behavior in a task-free social context assessing their attachment towards the owner. In random order, each dog participated in a non-invasive sleep electroencephalogram (EEG) measurement and in the Strange Situation Test (SST) to assess their attachment behavior. We found that higher attachment scores were associated with more time spent in NREM sleep, lower NREM alpha power activity and lower NREM alpha–delta anticorrelation. Our results reveal that, when dogs sleep in a novel environment in the company of their owners, differences in their attachment are reflected in their sleep EEG characteristics. This could be best explained by the different degree that owners could be used as a safe haven in an unfamiliar environment and during the unusual procedure of the first EEG measurement.
Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.
Electromyography (EMG) is a sophisticated electrodiagnostic-neurophysiological method, which serves to diagnose neuromuscular system diseases. It is based on the measurement of the electrical potentials created by the skeletal muscle activity. For this technique, surface electrodes and needle electrodes can be used, which read the action potential of a large number of motor units and read a small number of motor units, respectively. The wide-spectrum application of this method extends our diagnostic possibilities of the clinical examination in veterinary practice. Together with a clinical neurological examination and imaging methods, EMG forms a part of the diagnosis of nervous system diseases and it is a useful diagnostic technique for differentiating neuropathies, junctionopathies, and myopathies. The results of the neurophysiological examination inform us about the functional state of the peripheral and central nervous system; it can demonstrate subclinical diseases and monitor the dynamics of changes in the functional state of individual nervous systems over time. In this article, we review the electromyographic method and its use in veterinary practice.
Family dogs are exposed to a continuous flow of human speech throughout their lives. However, the extent of their abilities in speech perception is unknown. Here, we used functional magnetic resonance imaging (fMRI) to test speech detection and language representation in the dog brain. Dogs (n = 18) listened to natural speech and scrambled speech in a familiar and an unfamiliar language. Speech scrambling distorts auditory regularities specific to speech and to a given language, but keeps spectral voice cues intact. We hypothesized that if dogs can extract auditory regularities of speech, and of a familiar language, then there will be distinct patterns of brain activity for natural speech vs. scrambled speech, and also for familiar vs. unfamiliar language. Using multivoxel pattern analysis (MVPA) we found that bilateral auditory cortical regions represented natural speech and scrambled speech differently; with a better classifier performance in longer-headed dogs in a right auditory region. This neural capacity for speech detection was not based on preferential processing for speech but rather on sensitivity to sound naturalness. Furthermore, in case of natural speech, distinct activity patterns were found for the two languages in the secondary auditory cortex and in the precruciate gyrus; with a greater difference in responses to the familiar and unfamiliar languages in older dogs, indicating a role for the amount of language exposure. No regions represented differently the scrambled versions of the two languages, suggesting that the activity difference between languages in natural speech reflected sensitivity to language-specific regularities rather than to spectral voice cues. These findings suggest that separate cortical regions support speech naturalness detection and language representation in the dog brain.
Age-related differences in dog sleep and the age at which dogs reach adulthood as indexed by sleep electrophysiology are unknown. We assessed, in (1) a Juvenile sample (n = 60) of 2–14-month-old dogs (weight range: 4–68 kg), associations between age, sleep macrostructure, and non-rapid eye movement (NREM) EEG power spectrum, whether weight moderates associations, and (2) an extended sample (n = 91) of 2–30-months-old dogs, when sleep parameters stabilise. In Juvenile dogs, age was positively associated with time in drowsiness between 2 and 8 months, and negatively with time in rapid eye movement (REM) sleep between 2 and 6 months. Age was negatively associated with delta and positively with theta and alpha power activity, between 8 and 14 months. Older dogs exhibited greater sigma and beta power activity. Larger, > 8-month-old dogs had less delta and more alpha and beta activity. In extended sample, descriptive data suggest age-related power spectrum differences do not stabilise by 14 months. Drowsiness, REM, and delta power findings are consistent with prior results. Sleep electrophysiology is a promising index of dog neurodevelopment; some parameters stabilise in adolescence and some later than one year. Determination of the effect of weight and timing of power spectrum stabilisation needs further inquiry. The dog central nervous system is not fully mature by 12 months of age.
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.
The ancient partnership between people and dogs is struggling to meet modern day needs, with demand exceeding our capacity to safely breed high-performing and healthy dogs. New statistical genetic approaches and genomic technology have the potential to revolutionize dog breeding, by transitioning from problematic phenotypic selection to methods that can preserve genetic diversity while increasing the proportion of successful dogs. To fully utilize this technology will require ultra large datasets, with hundreds of thousands of dogs. Today, dog breeders struggle to apply even the tools available now, stymied by the need for sophisticated data storage infrastructure and expertise in statistical genetics. Here, we review recent advances in animal breeding, and how a new approach to dog breeding would address the needs of working dog breeders today while also providing them with a path to realizing the next generation of technology. We provide a step-by-step guide for dog breeders to start implementing estimated breeding value selection in their programs now, and we describe how genotyping and DNA sequencing data, as it becomes more widely available, can be integrated into this approach. Finally, we call for data sharing among dog breeding programs as a path to achieving a future that can benefit all dogs, and their human partners too.
Simple Summary
Dogs are currently involved in various roles in our society beyond companionship. The tasks humans assign to them impact their daily life and can sometimes create stressful situations, possibly jeopardizing their welfare. For example, assistance dogs need to manage their emotions in various challenging situations and environments. Thus, the capacity to cope with emotional stress is highly desirable in assistance dogs (~40% of assistance dogs fail to complete their education program). The emotional and stress responses are guided by brain processes involving neuromodulators. Neurohormonal profiling of these dogs can: (i) give cues about their emotional suitability to fulfill an assistance role; (ii) enhance their selection; and (iii) help to assess and improve their welfare state during the training course. We compared basal blood levels of three neuromodulators of interest between two populations, assistance vs. pet dogs. We found significantly different concentrations of oxytocin, a neuromodulator involved in social behavior. Levels of prolactin, a putative marker of chronic stress, were higher (although not statistically significant) and variable in assistance dogs. Dogs’ age also seemed to influence the various neuromodulators levels. These findings highlight the impact of different lifestyles undergone by dogs and the possibility to use neurohormonal profiling to monitor their effect on the dogs’ welfare and stress state.
Abstract
Assistance dogs must manage stress efficiently because they are involved in challenging tasks. Their welfare is currently a fundamental issue. This preliminary study aimed to compare assistance dogs (AD; n = 22) with pet dogs (PD; n = 24), using blood neuromodulator indicators to help find biomarkers that can improve the AD breeding, selection, training, and welfare monitoring. Both populations originated from different breeds, are of different ages, and had different lifestyles. Basal peripheral concentrations of prolactin (PRL), serotonin (5-HT), free (fOT) and total (tOT) oxytocin were measured by immunoassays. Multiple linear regressions were performed to assess the effect of activity, age, sex, and their interactions on these parameters. Correlations between neurohormonal levels were analyzed. No interactions were significant. fOT and tOT concentrations were significantly influenced by age (p < 0.0001 and p = 0.0002, respectively) and dogs’ activity (p = 0.0006 and p = 0.0277, respectively). A tendency was observed for age effect on PRL (p = 0.0625) and 5-HT (p = 0.0548), as well as for sex effect on tOT (p = 0.0588). PRL concentrations were heterogenous among AD. fOT and tOT were significantly but weakly correlated (Pearson’s r = 0.34; p = 0.04). Blood prolactin, serotonin, and oxytocin may represent biomarkers to assess workload and chronic stress-related responses in ADs and eventually improve their selection and training.
Simple Summary
Dogs have an extraordinary olfactory capability, which far exceeds that of humans. Dogs’ sense of smell seems to be the main sense, allowing them to not only gather both current and historical information about their surrounding environment, but also to find the source of the smell, which is crucial for locating food, danger, or partners for reproduction. Dogs can be trained by humans to use their olfactory abilities in a variety of fields, with a detection limit often much lower than that of sophisticated laboratory instruments. The specific anatomical and physiological features of dog olfaction allow humans to achieve outstanding results in the detection of drugs, explosives, and different illnesses, such as cancer, diabetes, or infectious disease. This article provides an overview of the anatomical features and physiological mechanisms involved in the process of odor detection and identification, as well as behavioral aspects of canine olfaction and its use in the service of humans in many fields.
Abstract
Olfaction in dogs is crucial for gathering important information about the environment, recognizing individuals, making decisions, and learning. It is far more specialized and sensitive than humans’ sense of smell. Using the strength of dogs’ sense of smell, humans work with dogs for the recognition of different odors, with a precision far exceeding the analytical capabilities of most modern instruments. Due to their extremely sensitive sense of smell, dogs could be used as modern, super-sensitive mobile area scanners, detecting specific chemical signals in real time in various environments outside the laboratory, and then tracking the odor of dynamic targets to their source, also in crowded places. Recent studies show that dogs can detect not only specific scents of drugs or explosives, but also changes in emotions as well as in human cell metabolism during various illnesses, including COVID-19 infection. Here, we provide an overview of canine olfaction, discussing aspects connected with anatomy, physiology, behavioral aspects of sniffing, and factors influencing the olfactory abilities of the domestic dog (Canis familiaris).
In humans, social relationship with the speaker affects neural processing of speech, as exemplified by children's auditory and reward responses to their mother's utterances. Family dogs show human analogue attachment behavior towards the owner, and neuroimaging revealed auditory cortex and reward center sensitivity to verbal praises in dog brains. Combining behavioral and non-invasive fMRI data, we investigated the effect of dogs’ social relationship with the speaker on speech processing. Dogs listened to praising and neutral speech from their owners and a control person. We found positive correlation between dogs’ behaviorally measured attachment scores towards their owners and neural activity increase for the owner's voice in the caudate nucleus; and activity increase in the secondary auditory caudal ectosylvian gyrus and the caudate nucleus for the owner's praise. Through identifying social relationship-dependent neural reward responses, our study reveals similarities in neural mechanisms modulated by infant-mother and dog-owner attachment.
Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.
Dogs are trained for a variety of working roles including assistance, protection, and detection work. Many canine working roles, in their modern iterations, were developed at the turn of the 20th century and training practices have since largely been passed down from trainer to trainer. In parallel, research in psychology has advanced our understanding of animal behavior, and specifically canine learning and cognition, over the last 20 years; however, this field has had little focus or practical impact on working dog training. The aims of this narrative review are to (1) orient the reader to key advances in animal behavior that we view as having important implications for working dog training, (2) highlight where such information is already implemented, and (3) indicate areas for future collaborative research bridging the gap between research and practice. Through a selective review of research on canine learning and behavior and training of working dogs, we hope to combine advances from scientists and practitioners to lead to better, more targeted, and functional research for working dogs.
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
Canine science is rapidly maturing into an interdisciplinary and highly impactful field with great potential for both basic and translational research. The articles in this Frontiers Research Topic, Our Canine Connection: The History, Benefits and Future of Human-Dog Interactions, arise from two meetings sponsored by the Wallis Annenberg PetSpace Leadership Institute, which convened experts from diverse areas of canine science to assess the state of the field and challenges and opportunities for its future. In this final Perspective paper, we identify a set of overarching themes that will be critical for a productive and sustainable future in canine science. We explore the roles of dog welfare, science communication, and research funding, with an emphasis on developing approaches that benefit people and dogs, alike.
Neuromonitoring in naturalistic environments is of increasing interest for a variety of research fields including psychology, economics, and productivity. Among functional neuromonitoring modalities, functional near-infrared spectroscopy (fNIRS) is well regarded for its potential for miniaturization, good spatial and temporal resolutions, and resilience to motion artifacts. Historically, the large size and high cost of fNIRS systems have precluded widespread adoption of the technology. In this article, we describe the first open source, fully integrated wireless fNIRS headband system with a single LED-pair source and four detectors. With ease of operation and comfort in mind, the system is encased in a soft, lightweight cloth and silicone enclosure. Accompanying computer and smartphone data collection software have also been provided, and the hardware has been validated using classic fNIRS tasks. This wear-and-go design can easily be scaled to accommodate a larger number of fNIRS channels and opens the door to easily collecting fNIRS data during routine activities in naturalistic conditions.
Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.
Significance: Neonates are a highly vulnerable population. The risk of brain injury is greater during the first days and weeks after birth than at any other time of life. Functional neuroimaging that can be performed longitudinally and at the cot-side has the potential to improve our understanding of the evolution of multiple forms of neurological injury over the perinatal period. However, existing technologies make it very difficult to perform repeated and/or long-duration functional neuroimaging experiments at the cot-side. Aim: We aimed to create a modular, high-density diffuse optical tomography (HD-DOT) technology specifically for neonatal applications that is ultra-lightweight, low profile and provides high mechanical flexibility. We then sought to validate this technology using an anatomically accurate dynamic phantom. Approach: An advanced 10-layer rigid-flexible printed circuit board technology was adopted as the basis for the DOT modules, which allows for a compact module design that also provides the flexibility needed to conform to the curved infant scalp. Two module layouts were implemented: dual-hexagon and triple-hexagon. Using in-built board-to-board connectors, the system can be configured to provide a vast range of possible layouts. Using epoxy resin, thermochromic dyes, and MRI-derived 3D-printed moulds, we constructed an electrically switchable, anatomically accurate dynamic phantom. This phantom was used to quantify the imaging performance of our flexible, modular HD-DOT system. Results: Using one particular module configuration designed to cover the infant sensorimotor system, the device provided 36 source and 48 detector positions, and over 700 viable DOT channels per wavelength, ranging from 10 to
∼
45
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over an area of approximately
60
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2
. The total weight of this system is only 70 g. The signal changes from the dynamic phantom, while slow, closely simulated real hemodynamic response functions. Using difference images obtained from the phantom, the measured 3D localization error provided by the system at the depth of the cortex was in the of range 3 to 6 mm, and the lateral image resolution at the depth of the neonatal cortex is estimated to be as good as 10 to 12 mm. Conclusions: The HD-DOT system described is ultra-low weight, low profile, can conform to the infant scalp, and provides excellent imaging performance. It is expected that this device will make functional neuroimaging of the neonatal brain at the cot-side significantly more practical and effective.
Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.
This study investigated the behavioral and neural indices of detecting facial familiarity and facial emotions in human faces by dogs. Awake canine fMRI was used to evaluate dogs’ neural response to pictures and videos of familiar and unfamiliar human faces, which contained positive, neutral, and negative emotional expressions. The dog–human relationship was behaviorally characterized out-of-scanner using an unsolvable task. The caudate, hippocampus, and amygdala, mainly implicated in reward, familiarity and emotion processing, respectively, were activated in dogs when viewing familiar and emotionally salient human faces. Further, the magnitude of activation in these regions correlated with the duration for which dogs showed human-oriented behavior towards a familiar (as opposed to unfamiliar) person in the unsolvable task. These findings provide a bio-behavioral basis for the underlying markers and functions of human–dog interaction as they relate to familiarity and emotion in human faces.
The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.
Background
Undernutrition is one of the most common problems among people living with HIV, contributing to premature death and the development of comorbidities within this population. In Sub-Saharan Africa (SSA), the impacts of these often inter-related conditions appear in a series of fragmented and inconclusive studies. Thus, this review examines the pooled effects of undernutrition on mortality and morbidities among adults living with HIV in SSA.
Methods
A systematic literature search was conducted from PubMed, EMBASE, CINAHL, and Scopus databases. All observational studies reporting the effects of undernutrition on mortality and morbidity among adults living with HIV in SSA were included. Heterogeneity between the included studies was assessed using the Cochrane Q-test and I ² statistics. Publication bias was assessed using Egger’s and Begg’s tests at a 5% significance level. Finally, a random-effects meta-analysis model was employed to estimate the overall adjusted hazard ratio.
Results
Of 4309 identified studies, 53 articles met the inclusion criteria and were included in this review. Of these, 40 studies were available for the meta-analysis. A meta-analysis of 23 cohort studies indicated that undernutrition significantly (AHR: 2.1, 95% CI: 1.8, 2.4) increased the risk of mortality among adults living with HIV, while severely undernourished adults living with HIV were at higher risk of death (AHR: 2.3, 95% CI: 1.9, 2.8) as compared to mildly undernourished adults living with HIV. Furthermore, the pooled estimates of ten cohort studies revealed that undernutrition significantly increased the risk of developing tuberculosis (AHR: 2.1, 95% CI: 1.6, 2.7) among adults living with HIV.
Conclusion
This review found that undernutrition has significant effects on mortality and morbidity among adults living with HIV. As the degree of undernutrition became more severe, mortality rate also increased. Therefore, findings from this review may be used to update the nutritional guidelines used for the management of PLHIV by different stakeholders, especially in limited-resource settings.
Although a positive link between sleep spindle occurrence and measures of post-sleep recall (learning success) is often reported for humans and replicated across species, the test-retest reliability of the effect is sometimes questioned. The largest to date study could not confirm the association, however methods for automatic spindle detection diverge in their estimates and vary between studies. Here we report that in dogs using the same detection method across different learning tasks is associated with observing a positive association between sleep spindle density (spindles/minute) and learning success. Our results suggest that reducing measurement error by averaging across measurements of density and learning can increase the visibility of this effect, implying that trait density (estimated through averaged occurrence) is a more reliable predictor of cognitive performance than estimates based on single measures.
fMRI has been increasingly used to study brain function in domestic dogs trained to lie still in MRI scanners. These studies highlight both similarities and differences between dogs and humans when presented with the same stimuli, raising intriguing questions about the concept of functional homologies in a coevolved species that shares the human environment.
Decision-making is a very important cognitive process in our daily life. There has been increasing interest in the discriminability of single-trial electroencephalogram (EEG) during decision-making. In this study, we designed a machine learning based framework to explore the discriminability of single-trial EEG corresponding to different decisions. For each subject, the framework split the decision-making trials into two parts, trained a feature model and a classifier on the first part, and evaluated the discriminability on the second part using the feature model and classifier. A proposed algorithm and five existing algorithms were applied to fulfill the feature models, and the algorithm Linear Discriminative Analysis (LDA) was used to implement the classifiers. We recruited 21 subjects to participate in Chicken Game (CG) experiments. The results show that there exists the discriminability of single-trial EEG between the cooperation and aggression decisions during the CG experiments, with the classification accuray of 75% (±6%), and the discriminability is mainly from the EEG information below 40 Hz. The further analysis indicates that the contributions of different brain regions to the discriminability are consistent with the existing knowledge on the cognitive mechanism of decision-making, confirming the reliability of the conclusions. This study exhibits that it is feasible to apply machine learning methods to EEG analysis of decision-making cognitive process.
Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8am-8pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2=17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.
In recent years the study of dynamic, between-brain coupling mechanisms has taken social neuroscience by storm. In particular, interbrain synchrony (IBS) is a putative neural mechanism said to promote social interactions by enabling the functional integration of multiple brains. In this article, I argue that this research is beset with three pervasive and interrelated problems. First, the field lacks a widely accepted definition of IBS. Second, IBS wants for theories that can guide the design and interpretation of experiments. Third, a potpourri of tasks and empirical methods permits undue flexibility when testing the hypothesis. These factors synergistically undermine IBS as a theoretical construct. I finish by recommending measures that can address these issues.
The basic method for imaging of the functional states of the brain involves recordings of electrical activity, i.e. EEG. So far, a majority of EEG recordings in animals have been performed with the use of pharmacological agents restricting animals’ mobility. Assessment of brain electrical activity in animal brain in welfare conditions offers new possibilities of analysis of behaviour, including social and utility-related behaviour. The investigations were conducted on two female trained and untrained dogs. The recordings of the EEG signal were made at full consciousness of the central nervous system and animals’ unrestrained static position. EEG readings at voice-command and complete-silence sessions were recorded. Analysis of the EEG signal frequency in the case of the untrained dog showed maximum amplitude at 17 Hz (beta 1 - beta 2 range), which suggested increased brain activity in response to voice commands. Response of trained female to the sound stimulus was manifested by the dominant components in the beta 3 range (20 Hz - 28 Hz); moreover, there were no periods of dominant alpha rhythm in that measurement. The dog was calm but waited for the commands attentively and actively. However, in the untrained dog during periods of relaxation and rest associated with the presence of a guardian, frequencies close to the alpha rhythm were noted (8 Hz – 14 Hz).
Recognition of olfactory-induced electroencephalogram (EEG) signals can provide an effective means for the research on disorder diagnostics and human–machine interaction. A novel triangular hashing (TH) approach is proposed for EEG signal recognition. The TH approach consists of a triangular feature construction and a hash inspired coding idea, which makes effective use of the feature differences between EEG electrodes. Firstly, a triangular feature set with N layers is constructed based on power-spectral density (PSD) features extracted from N electrodes for each frequency band of each olfactory EEG sample. Subsequently, the electrode orders, i.e. the TH codes for each layer of the constructed feature set are obtained by arranging the feature values in ascending order. Finally, the prediction type of the testing sample is determined by finding the most similar TH codes between EEG types and the testing sample. Experimental results reveal that for the recognition of olfactory EEG signals acquired from eleven subjects, the proposed TH recognition approach yields the considerably high accuracy of 93.0%, significantly superior to the other eight traditional methods. Besides, the EEG dataset with 5005 samples used in this study is made public through the website presented in this paper. In this way, the proposed TH method combined with the published EEG dataset may provide new perspectives for further study in olfactory EEG research.
To learn words, humans extract statistical regularities from speech. Multiple species use statistical learning also to process speech, but the neural underpinnings of speech segmentation in non-humans remain largely unknown. Here, we investigated computational and neural markers of speech segmentation in dogs, a phylogenetically distant mammal that efficiently navigates humans’ social and linguistic environment. Using electroencephalography (EEG), we compared event-related responses (ERPs) for artificial words previously presented in a continuous speech stream with different distributional statistics. Results revealed an early effect (220–470 ms) of transitional probability and a late component (590–790 ms) modulated by both word frequency and transitional probability. Using fMRI, we searched for brain regions sensitive to statistical regularities in speech. Structured speech elicited lower activity in the basal ganglia, a region involved in sequence learning, and repetition enhancement in the auditory cortex. Speech segmentation in dogs, similar to that of humans, involves complex computations, engaging both domain-general and modality-specific brain areas.
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Like using a substandard calibrant to test and calibrate an instrumental detector, when detection canines are regularly exposed to less than optimal training material, their detection proficiency is diminished, risking the lives of their handlers and civilians they are intended to protect. This research examined canine detection proficiency to odor mixtures and the use of mixture training to improve said proficiency. Trained detection canines were tested on their ability to correctly locate their trained target odors, explosives or narcotics, in various mixtures from a series of blanks and distractor odors. After making base measurements, canines were trained on the target odor in mixtures using the Mixed Odor Delivery Device (MODD), which was previously developed to safely contain separated explosive components and deliver the mixed odor to a canine detector for training purposes. Headspace measurements, made using solid phase microextraction with gas chromatography/mass spectrometry (SPME-GC/MS), were also taken of mixture components in and out of the MODD to confirm that odor mixtures were accurately portrayed to the canines during MODD training. Following mixture training, canines were retested on the same mixtures. Results of the headspace analysis showed that the MODD did not alter the delivery of the odorants from the mixture components. As such, canines showed an improved proficiency in detection of target mixtures following mixture training, increasing the detection rate from 63% to 72% for pseudo cocaine mixtures and from 19% to 100% for explosive mixtures.
The aim of this research was to discover if artificial neural networks can be used to classify pressure sensor data generated by medical detection dogs as they sniff biological samples. A detection dog can be trained to recognise the odour emitted by one of a wide range of diseases such as prostate cancer, malaria or, potentially, COVID-19. The dog searches a row of sample pots and indicates a positive sample by sitting in front of it. This offers a non-invasive means of diagnosing the specific cancer or disease that the dog has been trained to recognise. For this study, pressure sensors were attached to the sample pots to generate time series data pertaining to the dog’s searching behaviour as they press their nose against the sample pot to sniff its content. Automatic classification could provide a second form of indication, to support or refute the dog’s explicit signal (to sit at a positive sample), which is not always correct. Ultimately, classification software could eliminate the need for the dog to perform an indication gesture, making the dog’s task easier and training quicker.
Four different neural network architectures were evaluated: multilayer perceptron (MLP), a convolutional neural network (CNN), a fully convolutional network (FCN) and ResNet (a deep convolutional neural network). Each model was trained to classify the pressure data generated by medical detection dogs. To achieve a useful level of accuracy, it was found that the models needed to be trained using only those data samples where the dog had correctly classified the scent sample.
Model hyperparameters were tuned to improve accuracy. We found that the best performing model was MLP. When tested on previously unseen data, where the dog was not always correct, the classification performance of the MLP approached that of the medical detection dogs. For our particular dataset, the model’s true positive rate (i.e. recall) was 59%, matching that of the dogs. The model’s true negative rate was 79%, compared to the dogs’ 91%.
Neuronal oscillations route external and internal information across brain regions. In the olfactory system, the two central nodes-the olfactory bulb (OB) and the piriform cortex (PC)-communicate with each other via neural oscillations to shape the olfactory percept. Communication between these nodes have been well characterized in non-human animals but less is known about their role in the human olfactory system. Using a recently developed and validated EEG-based method to extract signals from the OB and PC sources, we show in healthy human participants that there is a bottom-up information flow from the OB to the PC in the beta and gamma frequency bands, while top-down information from the PC to the OB is facilitated by delta and theta oscillations. Importantly, we demonstrate that there was enough information to decipher odor identity above chance from the low gamma in the OB-PC oscillatory circuit as early as 100ms after odor onset. These data further our understanding of the critical role of bidirectional information flow in human sensory systems to produce perception. However, future studies are needed to determine what specific odor information is extracted and communicated in the information exchange.
As a relatively new field of neurology and computer science, brain computer interface (BCI) has many established and burgeoning applications across scientific disciplines. Many neural monitoring technologies have been developed for BCI studies. Combining multiple monitoring technologies provides a new approach that synthesizes the advantages and overcomes the limitations of each technology. This article presents a systematic review on the applications, limitations, and future directions for the hybridization of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) into one synchronous multimodality. This review investigated research questions on design and usability of hybrid EEG-fNIRS studies. In this article, 765 papers were included in the initial search and 128 papers were selected through the PRISMA protocol. The review results show the possibility of improving the performance of hybrid EEG-fNIRS by optimizing the feature extraction algorithms and physical designing as well as expending more possible applications in information processing related fields.