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Purpose This study aimed (1) to examine the role of potential correlates of HRQoL in a large representative sample of older adults, and (2) to investigate whether the relationships between HRQoL and potential factors differ as a function of HRQoL component (physical vs. mental) and/or age cohort (young-old vs. old–old). Methods This cross-sectional study included 802 older adults aged 60–79 years old. HRQoL was assessed using the SF-36 questionnaire. Functional fitness was assessed using the Senior Fitness Test. Physical activity was measured via the Baecke questionnaire. Demographic information, mental and health features were obtained through questionnaires. Results A multiple regression analysis showed that BMI (β = −0.15, p = 0.001), body strength (β = 0.21, p

[EN] Stroke can lead to motor impairments that can affect the body structure and restraint mobility. We hypothesize that brain lesions and their motor sequelae can distort the body schema, a sensorimotor map of body parts and elements in the peripersonal space through which human beings embody the reachable space and ready the body for forthcoming movements. Two main constructs have been identified in the embodiment mechanism: body-ownership, the sense that the body that one inhabits is his/her own, and agency, the sense that one can move and control his/her body. To test this, the present study simultaneously investigated different embodiment subcomponents (body-ownership, localization, and agency) and different neurophysiological measures (galvanic skin response, skin temperature, and surface electromyographic activity), and the interaction between them, in clinically-controlled hemiparetic individuals with stroke and in healthy subjects after the rubber hand illusion. Individuals with stroke reported significantly stronger body-ownership and agency and reduced increase of galvanic skin response, skin temperature, and muscular activity in the stimulated hand. We suggest that differences in embodiment could have been motivated by increased plasticity of the body schema and pathological predominance of the visual input over proprioception. We also suggest that differences in neurophysiological responses could have been promoted by a suppression of the reflex activity of the sympathetic nervous system and by the involvement of the premotor cortex in the reconfiguration of the body schema. These results could evidence a body schema plasticity promoted by the brain lesion and a main role of the premotor cortex in this mechanism., This work was supported by Ministerio de Economia y Competitividad of Spain (Project NeuroVR, TIN2013-44741-R, Project REACT, TIN2014-61975-EXP, and Grant BES-2014-068218), and by Universitat Politecnica de Valencia (Grant PAID-10-14).

The exponential increase of wearable health-tracking technologies offers new possibilities but also poses new challenges in signal processing to enable fitness monitoring through multimodal physiological recordings. Although there are several software tools used for post-processing in physiological computing applications, limitations in the analysis, incorporating signals from multiple sources, integrating contextual information and providing information visualization tools prevent a widespread use of this technology. To address these issues, we introduce PhysioLab, a multimodal processing Matlab tool for the data analysis of Electromyography (EMG), Electrocardiography (ECG) and Electrodermal Activity (EDA). The software is intended to facilitate the processing and comprehension of multimodal physiological data with the aim of assessing fitness in several domains. A unique feature of PhysioLab is that is informed by normative data grouped by age and sex, allowing contextualization of data based on users’ demographics. Besides signal processing, PhysioLab includes a novel approach to multivariable data visualization with the aim of simplifying interpretation by non-experts users. The system computes a set of ECG features based on heart rate variability analysis, EMG parameters to quantify force and fatigue levels, and galvanic skin level/responses from EDA signals. Furthermore, PhysioLab provides compatibility with data from multiple low-cost wearable sensors. We conducted an experiment with 17 community-dwelling older adults (64.5 ± 6.4) to assess the feasibility of the tool in characterizing cardiorespiratory profiles during physical activity. Correlation analyses and regression models showed significant interactions between physiology and fitness evaluations. Our results suggest novel ways that physiological parameters could be effectively used to complement traditional fitness assessment.

Background: Cognitive impairments after stroke are not always given sufficient attention despite the critical limitations they impose on activities of daily living (ADLs). Although there is substantial evidence on cognitive rehabilitation benefits, its implementation is limited because of time and human resource’s demands. Moreover, many cognitive rehabilitation interventions lack a robust theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and-pencil tasks, parameterized according to patients' needs. Objective: In this study, we aimed to present a framework for the creation of personalized cognitive rehabilitation tasks based on a participatory design strategy. Methods: We selected 11 paper-and-pencil tasks from standard clinical practice and parameterized them with multiple configurations. A total of 67 tasks were assessed according to their cognitive demands (attention, memory, language, and executive functions) and overall difficulty by 20 rehabilitation professionals. Results: After assessing the internal consistency of the data—that is, alpha values from .918 to .997—we identified the parameters that significantly affected cognitive functions and proposed specific models for each task. Through computational modeling, we operationalized the tasks into their intrinsic parameters and developed a Web tool that generates personalized paper-and-pencil tasks—the Task Generator (TG). Conclusions: Our framework proposes an objective and quantitative personalization strategy tailored to each patient in multiple cognitive domains (attention, memory, language, and executive functions) derived from expert knowledge and materialized in the TG app, a cognitive rehabilitation Web tool.

Social factors and motivation are key factors for recovery in stroke patients (Glass, Matchar, Belyea, & Feussner, 1993). The goal of this study is to enhance accessibility and evaluate the effects of including social interaction in a virtual reality (VR) -based system for stroke rehabilitation. We hypothesize that a multiplayer competitive context will have a positive effect on the involvement of the patients in the therapy and thus on the rehabilitation process. We test this hypothesis using the Rehabilitation Gaming System (RGS), an ICT virtual reality tool for upper extremities motor rehabilitation. First, we implemented and evaluated a new interface based on a low-cost key-glove. Then, we developed a dedicated RGS scenario where the player has to match pairs of cards from a stack of playing cards. This task trains cognitive (memory) and motor tasks (grasping and reaching). Eight stroke patients participated in two sessions lasting 20 min, one using a single-player VR environment and another using a multiplayer version of the same game. A usability test showed that participants interact with the system much faster when using the new key-glove–based interface (p = .02) in comparison to a mouse and keyboard. In addition, our results showed that upper limb exercises performed by the patients in multiplayer mode reached wider elbow flexion/extension movements than the ones performed during the single-player game session (p = .04). Considering that the presence of spasticity is very common in patients affected by an ictus and that it causes an ongoing level of contraction, these results suggest that the patients affected displayed more effort in reaching if engaged in a social task. Our study shows that accessibility and social engagement in multiplayer environments positively affects the patients' performance and enjoyment during the task. Although the long-term impact of this enhanced motivation needs to be further assessed, our results do suggest that the inclusion of social factors such as multiplayer capabilities is an important factor for the rehabilitation process in VR-based therapy and might have an impact on both performance and mood of stroke patients.

Exergames are exercise-oriented games that offer opportunities to increase motivation for exercising and improving health benefits. However, Exergames need to be adaptive and provide accurate feedback for physiologically correct exercising, sustaining motivation and for better personalized experiences. To investigate the role of physiological computing in those aspects, we employed a repeated measures design exploring changes in physiological responses caused by the gaming and exercising components of an Exergame intervention. Seventeen older adults (64.5±6.4 years) interacted with a videogame in two modes (Control, Exergaming) in different difficulty levels. Electrocardiography, Electrodermal and Kinematic data were gathered synchronously with game data. Findings show that Exercise intensities and heart rate changes were largely modulated by game difficulty, and positive feedback was more likely to produce arousal responses during Exergaming than negative feedback. A heart rate- variability analysis revealed strong influences of the interaction mode showing that Exergaming has potential to enhance cardiac regulation. Our results bring new insights on the usefulness of psychophysiological methods to sustain exercising motivation and personalizing gameplay to the individual needs of users in Exergaming experiences.

In recent years, Brain-Computer Interfaces (BCIs) have been steadily gaining ground in the market, used either as an implicit or explicit input method in computers for accessibility, entertainment or rehabilitation. Past research in BCI has heavily neglected the human aspect in the loop, focusing mostly in the machine layer. Further, due to the high cost of current BCI systems, many studies rely on low-cost and low-quality equipment with difficulties to provide significant advancements in physiological computing. Open-Source projects are offered as alternatives to expensive medical equipment. Nevertheless, the effectiveness of such systems over their cost is still unclear, and whether they can deliver the same level of experience as their more expensive counterparts. In this paper, we demonstrate that effective BCI interaction in a Motor-Imagery BCI paradigm can be accomplished without requiring high-end/high-cost devices, by analyzing and comparing EEG systems ranging from open source devices to medically certified systems.

Background The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training. Methods In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence. Results Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience. Conclusions Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.

In the recent years, the use of virtual reality (VR) to enhance motor skills of persons with activity and participation restriction due to disease or injury has been become an important area of research. In this chapter, we describe the design of such VR systems and their underlying principles, such as experience-dependent neuroplasticity and motor learning. Further, psychological constructs related to motivation including salience, goal setting, and rewards are commonly utilized in VR to optimize motivation during rehabilitation activities. Hence, virtually simulated activities are considered to be ideal for (1) the delivery of specific feedback, (2) the ability to perform large volumes of training, and (3) the presentation of precisely calibrated difficulty levels, which maintain a high level of challenge throughout long training sessions. These underlying principles are contrasted with a growing body of research comparing the efficacy of VR with traditionally presented rehabilitation activities in persons with stroke that demonstrate comparable or better outcomes for VR. In addition, a small body of literature has utilized direct assays of neuroplasticity to evaluate the effects of virtual rehabilitation interventions in persons with stroke. Promising developments and findings also arise from the use of off-the-shelf video game systems for virtual rehabilitation purposes and the integration of VR with robots and brain-computer interfaces. Several challenges limiting the translation of virtual rehabilitation into routine rehabilitation practice need to be addressed but the field continues to hold promise to answer key issues faced by modern healthcare.

Background Virtual Reality (VR) based methods for stroke rehabilitation have mainly focused on motor rehabilitation, but there is increasing interest in integrating motor and cognitive training to increase similarity to real-world settings. Unfortunately, more research is needed for the definition of which type of content should be used in the design of these tools. One possibility is the use of emotional stimuli, which are known to enhance attentional processes. According to the Socioemotional Selectivity Theory, as people age, the emotional salience arises for positive and neutral, but not for negative stimuli. Methods For this study we developed a cognitive-motor VR task involving attention and short-term memory, and we investigated the impact of using emotional images of varying valence. The task consisted of finding a target image, shown for only two seconds, among fourteen neutral distractors, and selecting it through arm movements. After performing the VR task, a recall task took place and the patients had to identify the target images among a valence-matched number of distractors. Ten stroke patients participated in a within-subjects experiment with three conditions based on the valence of the images: positive, negative and neutral. Eye movements were recorded during VR task performance with an eye tracking system. Results Our results show decreased attention for negative stimuli in the VR task performance when compared to neutral stimuli. The recall task shows significantly more wrongly identified images (false memories) for negative stimuli than for neutral. Regression and correlation analyses with the Montreal Cognitive Assessment and the Geriatric Depression Scale revealed differential effects of cognitive function and depressive symptomatology in the encoding and recall of positive, negative and neutral images. Further, eye movement data shows reduced search patterns for wrongly selected stimuli containing emotional content. Conclusions The results of this study suggest that it is feasible to use emotional content in a VR based cognitive-motor task for attention and memory training after stroke. Stroke survivors showed less attention towards negative information, exhibiting reduced visual search patterns and more false memories. We have also shown that the use of emotional stimuli in a VR task can provide additional information regarding patient’s mood and cognitive status.

Returning to the point of departure after exploring the environment is a key capability for most animals. In the absence of landmarks, this task will be solved by integrating direction and distance traveled over time. This is referred to as path integration or dead reckoning. An important question is how the nervous systems of navigating animals such as the 1 mm(3) brain of ants can integrate local information in order to make global decision. In this article we propose a neurobiologically plausible system of storing and retrieving direction and distance information. The path memory of our model builds on the well established concept of population codes, moreover our system does not rely on trigonometric functions or other complex non-linear operations such as multiplication, but only uses biologically plausible operations such as integration and thresholding. We test our model in two paradigms; in the first paradigm the system receives input from a simulated compass, in the second paradigm, the model is tested against behavioral data recorded from 17 ants. We were able to show that our path memory system was able to reliably encode and compute the angle of the vector pointing to the start location, and that the system stores the total length of the trajectory in a dependable way. From the structure and behavior of our model, we derive testable predictions both at the level of observable behavior as well as on the anatomy and physiology of its underlying neuronal substrate.

VR-based methods for stroke rehabilitation have mainly focused on motor rehabilitation, but there is increasing interest towards the integration of cognitive training for providing more ecologically valid solutions. However, more studies are needed, especially in the definition of which type of content should be used in the design of these tools. One possibility is the use of emotional stimuli, which are known to enhance attentional processes. According to the Socio-emotional Selectivity Theory, as people age, this emotional salience arises for positive and neutral, but not for negative stimuli. Conversely, negative stimuli can be better remembered. In this study, we investigated the impact of using emotional stimuli with positive, negative and neutral valence in a VR cognitive and motor attention task. Ten stroke patients participated in a within-subjects experiment with four conditions based on the type of stimuli: abstract (control condition), positive, negative and neutral. The main task consisted of finding a target stimulus, shown for only two seconds, among fourteen neutral distractors. Eye movements were recorded with an eye-tracking system to investigate differences between conditions and in search patterns. Subsequently, a recall task took place and the patients had to identify all the target images among a valence-matched number of distractors. Our results corroborate the attention salience effect of positive and neutral stimuli in the VR task performance. Although we found no statistically significant differences between conditions in the recall task, there was a trend for recalling more negative images. This negative advantage comes at the expense of significantly more wrongly identified images/false memories for negative stimuli. Finally, we performed an analysis in which we relate performance scores with well-established cognitive assessment instruments, which supportsG the use of this approach both for assessment and rehabilitation purposes.

Stroke is among the leading causes of long-term disability, leaving an increasing number of people with cognitive and motor impairments, loss of independence in their daily life and with a high societal cost. So far, the development of Brain-Computer Interfaces (BCIs) that translate brain activity into control signals in computers or external devices provide new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity and recovery even in severe chronic stroke. However, it is not fully clear how we can exploit the neurobiological mechanisms underlying recovery. This is the case for restorative BCI research. There is currently no standardized and accepted treatment for the use of BCIs with patients suffering from acute or chronic motor impairments. In this study we investigated with 9 healthy participants the role of multimodal virtual reality (VR) simulations and motor priming (MP) in a motor imagery BCI training. Our findings show improved BCI performance for VR and MP conditions, as well as the capacity to modulate and enhance brain activity patterns. Our data suggest that both VR and MP can be useful to promote neural activation and neuroplastic changes in the rehabilitation of stroke patients in a motor imagery neurofeedback paradigm.

Upper limb motor deficits caused by stroke have a large impact on a person’s daily activities and independence. The personalization of the rehabilitation tasks to the needs of the patient as well as the enhancement of the feedback provided to the patient are strategies for promoting motor relearning. In this paper we describe the development and pilot evaluation of a portable system that uses a robotic orthosis to deliver assistance and meaningful feedback during rehabilitative training. Two software modules are implemented, one that investigates an optimal calibration method for the personalization of the level of assistance, and another one that combines the orthosis with a mobile application running on a tablet that provides graphical knowledge of performance feedback to stroke patients while performing therapy. Here we present two preliminary studies and discuss the potential of this technology.

Cognitive impairments impose important limitations in the performance of activities of daily living. Although there is important evidence on cognitive rehabilitation benefits, its implementation is limited due to time and human resources demands. Moreover, many cognitive rehabilitation interventions lack a solid theoretical framework in the selection of paper-and-pencil tasks by the clinicians. In this endeavor, it would be useful to have a tool that could generate standardized paper-and-pencil tasks, parameterized according to patients' needs. In this paper, we combine the advantages of information and communication technologies with a participatory design approach with 20 health professionals to develop a novel cognitive rehabilitation web-tool: the NeuroRehabLab Task Generator (NTG). The NTG is a free and online available tool that systematically addresses multiple cognitive domains, and easily generates highly personalized paper-and-pencil training tasks. A field evaluation of the NTG with ten stroke patients showed moderate and strong correlations of patients' task performance with their cognitive assessment in multiple cognitive domains. That is, corroborating its usefulness both as an assessment and as rehabilitation tool.

Worldwide, more than one in three adults suffers from a cardiovascular disease. According to the World Health Organization, 15 million people experience a stroke each year and, of these, 5 million stay permanently disabled. The current limitations of traditional rehabilitation methods push towards the design of personalized tools that can be used intensively by patients and therapists in clinical or at-home environments. In this paper we present the design, implementation and validation of RehabCity, an online game designed for the rehabilitation of cognitive deficits through a gamified approach on activities of daily living (ADLs). Among other findings, our results show a strong correlation between the RehabCity scoring system and the Mini Mental State Examination test for clinical assessment of cognitive function in several domains. These findings suggest that RehabCity is a valid tool for the quantitative assessment of patients with cognitive deficits derived from a brain lesion.

We introduce a novel rehabilitation technology for upper limb rehabilitation after stroke that combines a virtual reality (VR) training paradigm with a myoelectric robotic limb orthosis. Our rehabilitation system is based on clinical guidelines and is designed to recruit specific motor networks to promote neuronal reorganization. The main hypothesis is that the restoration of active move- ment facilitates the full engagement of motor control networks during motor training. By using a robotic limb orthosis, we are able to restore active arm movement in severely affected stroke patients. In a pilot evaluation, we have successfully deployed and assessed our system with three chronic stroke patients by means of behavioral data and self-report questionnaires. The results show that our system is able to restore up to 60% of the active movement capability of patients. Further, we show that we can assess the specific contribution of the biceps/triceps movement of the paretic arm in a VR bilateral training task. Question- naire data show enjoyment and acceptance of the devel- oped rehabilitation system and its VR training task.

Upper limb motor deficits caused by stroke have a big impact on a person's daily activities and independence. One strategy for promoting motor relearning consists on the delivery of meaningful feedback during rehabilitative training. In this paper we describe the development and first evaluation of a system that combines a portable arm orthosis device and a mobile application running on a tablet in order to provide knowledge of performance to stroke patients while performing therapy. Here we present preliminary results and discuss the potential of this technology.

Elderly population will become the largest age group of our society in the next twenty years. Consequently, we need to be able to accommodate technologies to the needs of this population. AdaptNow is a web-based application that allows users to adapt existing webpages and turn them more accessible and user friendly. Users can do so directly from any web browser thanks to AdaptNow’s user personalization and automatic adaptation artificial intelligence algorithms. In this paper we present the design and implementation of AdaptNow, a solution that improves navigation on the web for elderly users.

The concept of a partially shared neural circuitry between action observation and action execution in healthy participants has been demonstrated through a number of studies. However, little research has been done in this regard utilizing eye movement metrics in rehabilitation contexts. In this study we approach action observation and action execution by combining a virtual environment and eye tracking technology. Participants consisted of stroke survivors, and were required to perform a simple reach-and-grab and place-and-release task with both their paretic and non-paretic arm. Results showed congruency in gaze metrics between action execution and action observation, for distribution and duration of gaze events. Furthermore, in action observation, longer smooth pursuit segments were detected when observing the representation of the paretic arm, thus providing evidence that the affected circuitry may be activated during observation of the simulated action. These results can lead to novel rehabilitation methods using virtual reality technology.

Every year millions of people worldwide suffer from stroke, resulting in motor and/or cognitive disability. As a result, patients experience an increased loss of independence, autonomy and low self-esteem. Evolving to a chronic condition, stroke requires of continuous rehabilitation and therapy. Current ICT approaches, with the use of robotics and Virtual Reality, show some benefits over conventional therapy. However, most of the novel approaches are suitable only for a reduced subset of patients. RehabNet proposes an inclusive approach towards an open and distributed architecture for `in-home' neuro-rehabilitation and monitoring by means of non-invasive ICT. In this paper we present the RehabNet architecture, its design and the implementation of a combined motor-and-cognitive system for post-stroke rehabilitation.

Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.

Background and Purpose— Although there is strong evidence on the beneficial effects of virtual reality (VR)-based rehabilitation, it is not yet well understood how the different aspects of these systems affect recovery. Consequently, we do not exactly know what features of VR neurorehabilitation systems are decisive in conveying their beneficial effects. Methods— To specifically address this issue, we developed 3 different configurations of the same VR-based rehabilitation system, the Rehabilitation Gaming System, using 3 different interface technologies: vision-based tracking, haptics, and a passive exoskeleton. Forty-four patients with chronic stroke were randomly allocated to one of the configurations and used the system for 35 minutes a day for 5 days a week during 4 weeks. Results— Our results revealed significant within-subject improvements at most of the standard clinical evaluation scales for all groups. Specifically we observe that the beneficial effects of VR-based training are modulated by the use/nonuse of compensatory movement strategies and the specific sensorimotor contingencies presented to the user, that is, visual feedback versus combined visual haptic feedback. Conclusions— Our findings suggest that the beneficial effects of VR-based neurorehabilitation systems such as the Rehabilitation Gaming System for the treatment of chronic stroke depend on the specific interface systems used. These results have strong implications for the design of future VR rehabilitation strategies that aim at maximizing functional outcomes and their retention. Clinical Trial Registration— This trial was not registered because it is a small clinical study that evaluates the feasibility of prototype devices.

How can odor-guided behavior of numerous individual Drosophila be assessed automatically with high temporal resolution? For this purpose we introduce the automatic integrated tracking and odor-delivery system Flywalk. In fifteen aligned small wind tunnels individual flies are exposed to repeated odor pulses, well defined in concentration and timing. The flies' positions are visually tracked, which allows quantification of the odor-evoked walking behavior with high temporal resolution of up to 100 ms. As a demonstration of Flywalk we show that the flies' behavior is odorant-specific; attractive odors elicit directed upwind movements, while repellent odors evoke decreased activity, followed by downwind movements. These changes in behavior differ between sexes. Furthermore our findings show that flies can evaluate the sex of a conspecific and males can determine a female's mating status based on olfactory cues. Consequently, Flywalk allows automatic screening of individual flies for their olfactory preference and sensitivity. This study was supported by fundings from the Max Planck Society to BSH.

A wide range of neuroscientific studies suggest the existence of cognitive mechanisms like attention, prediction, anticipation and strong vertical interactions between different hierarchical layers of the brain while performing complex tasks. Despite advances in both cognitive brain research and in the development of brain-inspired artificial cognitive systems, the interplay of these key ingredients of cognition remain largely elusive and unquantified in complex real-world tasks. Furthermore, it has not yet been demonstrated how a self-contained hierarchical cognitive system acting under limited resource constraints can quantifiably benefit from the incorporation of top-down and bottom-up attentional mechanisms. In this context, an open fundamental question is how a data association mechanism can integrate bottom-up sensory information and top-down knowledge. Here, building on the Distributed Adaptive Control (DAC) architecture, we propose a single framework for integrating these different components of cognition and demonstrate the framework's performance in solving real-world and simulated robot tasks. Using the model we quantify the interactions between prediction, anticipation, attention and memory. Our results support the strength of a complete system that incorporates attention, prediction and anticipation mechanisms compared to incomplete systems for real-world and complex tasks. We unveil the relevance of transient memory that underlines the utility of the above mechanisms for intelligent knowledge management in artificial sensorimotor systems. These findings provide concrete predictions for physiological and psychophysical experiments to validate our model in biological cognitive systems.

Olfaction is a crucial sense for many living organisms. Many animals, especially insects, rely heavily on the olfactory sense for encoding and processing different chemical cues in order to perform several tasks such as foraging, predator avoidance, mate finding, communication etc.(22). Yet, olfaction has not been as widely studied as vision or the auditory system in insects. At the same time, robotic platforms capable of searching, locating and classifying odor sources in wind turbulence and in the presence of complex odors have diverse applications ranging from environmental monitoring (21), detection of explosives and other hazardous substances (19), land mine detection (2) to human search and rescue operations. The main challenge thereby is the stable and fast coding and decoding of odors and the localization of the sources (17). In our own recent work, we have proposed an insect-like mapless navigation mechanism which integrates surge-and-cast chemo search, path integration, wind detection and visual landmark navigation on an indoor mobile robot (28). Also, we have proposed a model based on insect navigation that is capable of navigating in highly dynamic environments and our model was compared directly to ant navigational data, with strikingly similar navigational behaviors (26). The problem of ambiguous information, particularly in the navigational context, is also addressed in our recent work (27). Beyond that, we have contributed significantly to modeling insect navigation and designing robotic systems such as: a model of the locust Lobula Giant Movement Detector (LGMD) tested on a high speed robot (29), moth-like odor localization for robots (30), control of an unmanned aerial vehicle using a neuronal model of a fly-locust brain (31; 32), moth-like optomotor anemotactic chemical search for robots (33), and a blimp flight control using a biologically inspired flight control system (34). Despite these advances, several biological systems with relatively simple nervous systems solve the odor localization and classification problem much more efficiently than their artificial counterparts: bees use odor to localize nests, ants use pheromone trails to organize foraging in swarms, lobsters use odor to locate food, the Escherichia bacteria use odors to locate nutrients, male moths use olfaction to locate female mates etc. The odor localization

Purpose: Given the incidence of stroke, the need has arisen to consider more self-managed rehabilitation approaches. A promising technology is Virtual Reality (VR). Thus far, however, it is not clear what the benefits of VR systems are when compared to conventional methods. Here we investigated the clinical impact of one such system, the Rehabilitation Gaming System (RGS), on the recovery time course of acute stroke. RGS combines concepts of action execution and observation with an automatic individualization of training. Methods. Acute stroke patients (n = 8) used the RGS during 12 weeks in addition to conventional therapy. A control group (n = 8) performed a time matched alternative treatment, which consisted of intense occupational therapy or non-specific interactive games. Results. At the end of the treatment, between-group comparisons showed that the RGS group displayed significantly improved performance in paretic arm speed that was matched by better performance in the arm subpart of the Fugl-Meyer Assessment Test and the Chedoke Arm and Hand Activity Inventory. In addition, the RGS group presented a significantly faster improvement over time for all the clinical scales during the treatment period. Conclusions. Our results suggest that rehabilitation with the RGS facilitates the functional recovery of the upper extremities and that this system is therefore a promising tool for stroke neurorehabilitation.

Therapy for motor recovery in stroke patients is strongly related to motivation and social factors [1, 2]. In this study, we evaluate the effects of social interaction in stroke rehabilitation.

Stroke is one of the leading causes of adult disability with a high economical and societal cost. In the last years, novel rehabilitation paradigms have been proposed to make use of the life-long plasticity of the brain to regain motor function. We have developed a hybrid BCI-VR system that explores the idea of combining a personalized motor training in a VR environment - exploiting brain mechanisms for action execution and observation - and a neuro-feedback paradigm - using mental imagery - as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracks. Here we present the development and validation experiments of the system. The EEG data on 9 naive healthy subjects shows that a simultaneous motor action and motor imagery paradigm is more effective in engaging cortical motor networks to a larger extend. In addition, we have tested and validated a motor imagery driven BCI-VR version of our system with 9 additional healthy subjects. The results show that users are capable of controlling a virtual avatar in a motor training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.

This work describes the data from navigation experiments with the mobile robot, equipped with the sensor array of three MOX gas sensors. Performed four series of measurements aim to explore the capabilities of sensor array to build the odour map with one or two odour sources in the wind tunnel space. It was demonstrated that the method based on Independent Component Analysis (ICA) is able to discriminate two odour sources, that in future can be used in the surge‐and‐cast robot navigation algorithm.

Autonomous robotic odour source classification and localization in real world environments is an essential step for applications such as humanitarian demining, environmental monitoring or search and rescue operations. However, at the moment this problem has only been solved by nature (e.g.: moths, bees, rats, dogs). Biological systems are capable and efficient at odour source localization in spite of the difficulties present in the real world such as turbulent environments, obstacles, predators or interfering odours. Here we aim at exploiting our understanding of the moth to solve this problem and we propose a biologically based model of moth behaviour. We implement our model on a robot that uses chemical sensors and we test its performance in a controlled environment. Further, we extend the behavioural model with a sensor front end that supports classification in order to deal with odour distractors. We show that our system is able to locate an odour source and map the chemical environment in the presence of distractors.

Dealing with high-throughput information systems is becoming an everyday problem in many fields of science, as technological advances improve our ability to gather data. In particular, the information encoding problem in high-dimensional spaces is a crucial aspect to consider. In fact, biological systems are known to be very efficient at encoding and processing high-dimensional information. Here we propose a biologically-based solution that mimics the neural processing performed by the Antennal Lobe of insects. Based on our understanding of this system, our model exploits plausible neural mechanisms to transform the massive and high-dimensional spatial and temporal input of the olfactory receptor neurons into a neural population encoding based on synchrony and frequency, consistent with known physiology. We demonstrate the capabilities of our Antennal Lobe model in the context of a classification task of different olfactory stimuli of varying concentrations. We show that the generated neural representation conveys both the identity and the concentration of each stimuli.

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons., Author Summary The tiny brains of insects of about 1mm3 smoothly control a flying platform while avoiding obstacles, regulating its distance to objects and search for objects of interest. This is largely achieved through a complex hierarchical processing of signals from the multitude of ommatidia in their eye to a set of highly specialized neurons that are optimized to respond to specific properties of the visual world. One of these neurons, the Lobula Giant Movement Detector (LGMD) of the locust, has been recently shown to perform a functional multiplication of its synaptic inputs. If true, that would make the LGMD neuron a unique and highly sophisticated neuron that raises questions about the non-linear operations other neurons in other neuronal systems would be able to perform. Hence it is crucial to understand its properties, its role in behaviour and to evaluate whether its responses can be explained in simpler terms. Our results emphasize the role of network architecture and distributed computation as opposed to local complex non-linear computation. We show that our model reliably reproduces the known properties of the LGMD and can be used to control a high-speed robot.

Using multimodal sensors to perceive the environment and subsequently performing intelligent sensor/motor allocation is of crucial interest for building autonomous systems. Such a capability should allow autonomous entities to (re)allocate their resources for solving their most critical tasks depending on their current state, sensory input and knowledge about the world. Architectures of artificial real-world systems with internal representation of the world and such dynamic motor allocation capabilities are invaluable for systems with limited resources. Based upon recent advances in attention research and psychophysiology we propose a general purpose selective attention mechanism that supports the construction of a world model and subsequent intelligent motor control. We implement and test this architecture including its selective attention mechanism, to build a probabilistic world model. The constructed world-model is used to select actions by means of a Bayesian inference method. Our method is tested in a multi-robot task, both in simulation and in the real world, including a coordination mission involving aerial and ground vehicles.

Navigation in unknown dynamic environments still remains a major challenge in robotics. Whereas insects like the desert ant with very limited computing and memory capacities solve this task with great efficiency. Thus, the understanding of the underlying neural mechanisms of insect navigation can inform us on how to build simpler yet robust autonomous robots. Based on recent developments in insect neuroethology and cognitive psychology, we propose a method for landmark navigation in dynamic environments. Our method enables the navigator to learn the reliability of landmarks using an expectation reinforcement method. For that end, we implemented a real-time neuronal model based on the Distributed Adaptive Control framework. The results demonstrate that our model is capable of learning the stability of landmarks by reinforcing its expectations. Also, the proposed mechanism allows the navigator to optimally restore its confidence when its expectations are violated. We also perform navigational experiments with real ants to compare with the results of our model. The behavior of the proposed autonomous navigator closely resembles real ant navigational behavior. Moreover, our model explains navigation in dynamic environments as a memory consolidation process, harnessing expectations and their violations.

The eXperience Induction Machine (XIM) is one of the most advanced mixed-reality spaces available today. XIM is an immersive space that consists of physical sensors and effectors and which is conceptualized as a general-purpose infrastructure for research in the field of psychology and human–artifact interaction. In this chapter, we set out the epistemological rational behind XIM by putting the installation in the context of psychological research. The design and implementation of XIM are based on principles and technologies of neuromorphic control. We give a detailed description of the hardware infrastructure and software architecture, including the logic of the overall behavioral control. To illustrate the approach toward psychological experimentation, we discuss a number of practical applications of XIM. These include the so-called, persistent virtual community, the application in the research of the relationship between human experience and multi-modal stimulation, and an investigation of a mixed-reality social interaction paradigm.

We present a novel biomimetic approach to mapless autonomous navigation based on insect neuroethology. We implemented and tested a real-time neuronal model based on the Distributed Adaptive Control framework. The model unifies different aspects of insect navigation and foraging including landmark recognition, chemical search, path integration and optimal memory usage. Consistent with recent findings the model supports navigation using heading direction information, thus precluding the use of global information. We tested our model using a mobile robot performing a foraging task. While foraging for chemical sources in a wind tunnel, the robot memorizes the followed trajectories, using information from landmarks and heading direction accumulators. After foraging, landmark navigation is tested with the odor source turned off. Our results show stability against robot kidnapping and generalization of homing behavior to stable mapless landmark navigation. This demonstrates that allocentric and efficient goal-oriented navigation strategies can be generated by relying on purely local information.

Virtual and mixed reality environments (VMRE) often imply full-body human-computer interaction scenarios. We used a public multimodal mixed reality installation, the Synthetic Oracle, and a between-groups design to study the effects of implicit (e.g., passively walking) or explicit (e.g., pointing) interaction modes on the users' emotional and engagement experiences, and we assessed it using questionnaires. Additionally, real-time arm motion data was used to categorize the user behavior and to provide interaction possibilities for the explicit interaction group. The results show that the online behavior classification corresponded well to the users' interaction mode. In addition, contrary to the explicit interaction, the engagement ratings from implicit users were positively correlated with a valence but were uncorrelated with arousal ratings. Interestingly, arousal levels were correlated with different behaviors displayed by the visitors depending on the interaction mode. Hence, this study confirms that the activity level and behavior of users modulates their experience, and that in turn, the interaction mode modulates their behavior. Thus, these results show the importance of the selected interaction mode when designing users' experiences in VMRE.

Stroke will become one of the main burdens of disease and loss of quality of life in the near future. However, we still have not found rehabilitation approaches that can scale up so as to face this challenge. Virtual reality based therapy systems are a great promise for directly addressing this challenge. Here we review different approaches that are based on this technology, their assumptions and clinical impact. We will focus on virtual reality based rehabilitation systems that combine hypotheses on the aftermath of stroke and the neuronal mechanisms of recovery that directly aims at addressing this challenge. In particular we will analyze the, so called, Rehabilitation Gaming System (RGS) that proposes the use of non-invasive multi-modal stimulation to activate intact neuronal systems that provide direct stimulation to motor areas affected by brain lesions. The RGS is designed to engage the patients in task specific training scenarios that adapt to their performance, allowing for an individualized training of graded difficulty and complexity. Although RGS stands for a generic rehabilitative approach it has been specifically tested for the rehabilitation of motor deficits of the upper extremities of stroke patients. In this chapter we review the main foundations and properties of the RGS, and report on the major findings extracted from studies with healthy and stroke subjects. We show that the RGS captures qualitative and quantitative data on motor deficits, and that this is transferred between real and VR tasks. Additionally, we show how the RGS uses the detailed assessment of the kinematics and performance of stroke patients to individualize the treatment. Subsequently, we will discuss how real-time physiology can be used to provide additional measures to assess the task difficulty and subject engagement. Finally, we report on preliminary results of an ongoing longitudinal study on acute stroke patients.

The odor search strategies of the moth have been researched since many decades. Many behavioral studies have described the behavior under well controlled conditions, making predictions on what the underlying mechanisms might be. However, it is almost impossible to asses these mechanisms directly since sensory and behavioral data on a freely behaving moth are very hard to obtain. Therefore, we propose a comparative study were the behavior of a robot is analyzed when controlled by a number of odor source localization models. Our results show that a system making use of stereo odor information outperforms some well‐established chemical search models.

Intelligent sensor/motor allocation is gaining in importance in many areas of robotics and autonomous systems. It allows the autonomous entity to allocate its resources for solving the currently most critical task depending on the entitypsilas current state, its sensory input and its acquired knowledge of the world. Such architectures which support dynamic motor allocation are invaluable for systems with limited resources. Biological systems also build and maintain a world-model to enable intelligent motor decision making. Based on recent advances in attention research and psychophysiology we propose a general purpose push-pull selective attention mechanism for building a world model and intelligent motor action control. We implement and test an architecture called A-BID, which is guided by a neural network implementation of a selective attention mechanism that is used to build a probabilistic world model. Using A-BID, the system performs at each time step the action that is optimal in the Bayesian sense.

We have developed a virtual reality based system for the rehabilitation of patients suffering from different neuropathologies such as those brought on by stroke and traumatic brain injury. Our Rehabilitation Gaming System, or RGS, uses a vision based motion capture system with gaming technologies and functionally combines active evaluation, with continuous monitoring and intensive training regimes tuned to the needs of individual patients. Here we assess the validity of the evaluation phase of the RGS by comparing the physical and virtual versions of a diagnostic reaching test with 6 stroke patients. Subsequently we illustrate the ability of our system to provide high resolution information at the level of individual performance.

Antipersonnel mines, weapons of cheap manufacture but lethal effect, have a high impact on the population even decades after the conflicts have finished. Here we investigate the use of a chemo-sensing Unmanned Aerial Vehicle (cUAV) for demining tasks. We developed a blimp based UAV that is equipped with a broadly tuned metal-thin oxide chemo-sensor. A number of chemical mapping strategies were investigated including two biologically based localization strategies derived from the moth chemical search that can optimize the efficiency of the detection and localization of explosives and therefore be used in the demining process. Additionally, we developed a control layer that allows for both fully autonomous and manual controlled flight, as well as for the scheduling of a fleet of cUAVs. Our results confirm the feasibility of this technology for demining in real-world scenarios and give further support to a biologically based approach where the understanding of biological systems is used to solve difficult engineering problems.