Rehabilitative devices for a top-down approach

In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

A Mobile App Delivering a Gamified Battery of Cognitive Tests Designed for Repeated Play (OU Brainwave): App Design and Cohort Study

Background: Mobile phone and tablet apps are an increasingly common platform to collect data. A key challenge for researchers has been participant “buy-in” and participant attrition for designs requiring repeated testing. Objective: The objective of this study was to develop and asses the utility of 1 – 2 minute versions of both classic and novel cognitive tasks within a user focussed and driven mobile phone and tablet app designed to encourage repeated play. Methods: A large sample (N = 13,979 at first data collection) participated in multiple, self-paced, sessions of working memory (N-back), spatial cognition (Mental rotation), sustained attentional focus (Persistent Vigilance task), and split attention (Multiple object tracking) tasks along with an implementation of a novel action learning task. A full morningness-eveningness questionnaire was also included. Data was collected across an 18 month period. While the app prompted reengagement at set intervals, each participant was free to repeatedly complete each task as many times as they wished. Results: We found a significant relationship between morningness and age (r = 0.298, n = 12755, p Conclusions: Using extremely short testing periods and permitting participants to decide their own level of engagement - both in terms of which gamified task they played, and how many sessions they completed - we were able to collect a substantial and valid dataset. We suggest that the success of OU brainwave should inform future research oriented apps - particularly in issues around balancing participant engagement with data fidelity

Game-based learning or game-based teaching?

Emerging technologies for learning report - Article exploring games based learning and its potential for edcuatio

System development guidelines from a review of motion-based technology for people with MCI or dementia

As the population ages and the number of people living with dementia or mild cognitive impairment (MCI) continues to increase, it is critical to identify creative and innovative ways to support and improve their quality of life. Motion-based technology has shown significant potential for people living with dementia or MCI by providing opportunities for cognitive stimulation, physical activity and participation in meaningful leisure activities, while simultaneously functioning as a useful tool for research and development of interventions. However, many of the current systems created using motion-based technology have not been designed specifically for people with dementia or MCI. Additionally, the usability and accessibility of these systems for these populations has not been thoroughly considered. This paper presents a set of system development guidelines derived from a review of the state of the art of motion-based technologies for people with dementia or MCI. These guidelines highlight three overarching domains of consideration for systems targeting people with dementia or MCI: (i) cognitive, (ii) physical, and (iii) social. We present the guidelines in terms of relevant design and use considerations within these domains and the emergent design themes within each domain. Our hope is that these guidelines will aid in designing motion-based software to meet the needs of people with dementia or MCI such that the potential of these technologies can be realized

Predictive analysis of auditory attention from physiological signals

In recent years, there has been considerable interest in recording physiological signals from the human body to investigate various responses. Attention is one of the key aspects that physiologists, neuroscientists, and engineers have been exploring. Many theories have been established on auditory and visual selective attention. To date, the number of studies investigating the physiological responses of the human body to auditory attention on natural speech is, surprisingly, very limited, and there is a lack of public datasets. Investigating such physiological responses can open the door to new opportunities, as auditory attention plays a key role in many cognitive functionalities, thus impacting on learning and general task performance. In this thesis, we investigated auditory attention on the natural speech by processing physiological signals such as Electroencephalogram (EEG), Galvanic Skin Response (GSR), and Photoplethysmogram (PPG). An experiment was designed based on the well established dichotic listening task. In the experiment, we presented an audio stimulus under different auditory conditions: background noise level, length, and semanticity of the audio message. The experiment was conducted with 25 healthy, non-native speakers. The attention score was computed by counting the number of correctly identified words in the transcribed text response. All the physiological signals were labeled with their auditory condition and attention score. We formulated four predictive tasks exploiting the collected signals: Attention score, Noise level, Semanticity, and LWR (Listening, Writing, Resting, i.e., the state of the participant). In the first part, we analysed all the user text responses collected in the experiment. The statistical analysis reveals a strong dependency of the attention level on the auditory conditions. By applying hierarchical clustering, we could identify the experimental conditions that have similar effects on attention score. Significantly, the effect of semanticity appeared to vanish under high background noise. Then, analysing the signals, we found that the-state-of-the-art algorithms for artifact removal were inefficient for large datasets, as they require manual intervention. Thus, we introduced an EEG artifact removal algorithm with tuning parameters based on Wavelet Packet Decomposition (WPD). The proposed algorithm operates with two tuning parameters and three modes of wavelet filtering: Elimination, Linear Attenuation, and Soft-thresholding. Evaluating the algorithm performance, we observed that it outperforms state-of-the-art algorithms based on Independent Component Analysis (ICA). The evaluation was based on the spectrum, correlation, and distribution of the signals along with the performance in predictive tasks. We also demonstrate that a proper tuning of the algorithm parameters allows achieving further better results. After applying the artifact removal algorithm on EEG, we analysed the signals in terms of correlation of spectral bands of each electrode and attention score, semanticity, noise level, and state of the participant LWR). Next, we analyse the Event-Related Potential (ERP) on Listening, Writing and Resting segments of EEG signal, in addition to spectral analysis of GSR and PPG. With this thesis, we release the collected experimental dataset in the public domain, in order for the scientific community to further investigate the various auditory processing phenomena and their relation with EEG, GSR and PPG responses. The dataset can be used also to improve predictive tasks or design novel Brain-Computer-Interface (BCI) systems based on auditory attention. We also use the deeplearning approach to exploit the spatial relationship of EEG electrodes and inter-subject dependency of a model. As a domain application, we finally discuss the implications of auditory attention assessment for serious games and propose a 3-dimensional difficulty model to design game levels and dynamically adapt the difficulty to the player status

Neuroscience-informed Auditory Training in Schizophrenia: A Final Report of the Effects on Cognition and Serum Brain-Derived Neurotrophic Factor.

ObjectiveWe previously reported the interim effects in a per protocol analysis of a randomized controlled trial of an innovative neuroscience-informed computerized cognitive training approach in schizophrenia. Here we report the effects of training on behavioral outcome measures in our final sample using an intent-to-treat analysis. We also report the effects on serum brain-derived neurotrophic factor (BDNF).MethodEighty-seven clinically stable participants with schizophrenia were randomly assigned to either targeted auditory training (AT, N=46) or a computer games control condition (CG, N=41). Participants were assessed on neurocognition, symptoms and functional outcome at baseline and after 50 hours of intervention delivered over 10 weeks. Serum BDNF was assessed at baseline, at 2 weeks, and at 10 weeks.ResultsAfter the intervention, AT participants showed significant gains in global cognition, speed of processing, verbal learning, and verbal memory, relative to CG participants, with no changes in symptoms or functioning. At baseline, schizophrenia participants had significantly lower-than-normal serum BDNF. AT participants showed a significant increase in serum BDNF compared to CG participants, and "normalized" levels by post training.ConclusionsParticipants with chronic schizophrenia made significant cognitive gains after 50 hours of intensive computerized training delivered as a stand-alone treatment, but no improvement in symptoms or functioning. Serum BDNF levels were significantly increased, and may serve as a peripheral biomarker for the effects of training. Future research must focus on: 1) Methods of integrating cognitive training with psychosocial treatments; 2) A deeper understanding of underlying neurophysiology in order to enhance critical mechanisms of action

Gamified cognitive control training for remitted depressed individuals : user requirements analysis

Background: The high incidence and relapse rates of major depressive disorder demand novel treatment options. Standard treatments (psychotherapy, medication) usually do not target cognitive control impairments, although these seem to play a crucial role in achieving stable remission. The urgent need for treatment combined with poor availability of adequate psychological interventions has instigated a shift toward internet interventions. Numerous computerized programs have been developed that can be presented online and offline. However, their uptake and adherence are oftentimes low. Objective: The aim of this study was to perform a user requirements analysis for an internet-based training targeting cognitive control. This training focuses on ameliorating cognitive control impairments, as these are still present during remission and can be a risk factor for relapse. To facilitate uptake of and adherence to this intervention, a qualitative user requirements analysis was conducted to map mandatory and desirable requirements. Methods: We conducted a user requirements analysis through a focus group with 5 remitted depressed individuals and individual interviews with 6 mental health care professionals. All qualitative data were transcribed and examined using a thematic analytic approach. Results: Results showed mandatory requirements for the remitted sample in terms of training configuration, technological and personal factors, and desirable requirements regarding knowledge and enjoyment. Furthermore, knowledge and therapeutic benefits were key requirements for therapists. Conclusions: The identified requirements provide useful information to be integrated in interventions targeting cognitive control in depression

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Spatial audio in small display screen devices

Our work addresses the problem of (visual) clutter in mobile device interfaces. The solution we propose involves the translation of technique-from the graphical to the audio domain-for expliting space in information representation. This article presents an illustrative example in the form of a spatialisedaudio progress bar. In usability tests, participants performed background monitoring tasks significantly more accurately using this spatialised audio (a compared with a conventional visual) progress bar. Moreover, their performance in a simultaneously running, visually demanding foreground task was significantly improved in the eye-free monitoring condition. These results have important implications for the design of multi-tasking interfaces for mobile devices