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The subject of this paper is the idea of Brain–Computer Interface (BCI). The main goal is to assess the potential impact of BCI on the design, use and evaluation of information retrieval systems operating in libraries.

The method of literature review was used to establish the state of research. The search according to accepted queries was carried out in the Scopus database and complementary in Google Scholar. To determine the state of research on BCI on the basis of library and information science, a specialist LISTA abstract database was also searched. The most current papers published in the years 2015–2019 in the English language or having at least an abstract in this language were taken into account.

The analysis showed that BCI issues are extremely popular in subject literature from various fields, mainly computer science, but practically does not occur in the context of using this technology in information retrieval systems.

Due to the fact that BCI solutions are not yet implemented in libraries and are rarely the subject of scientific considerations in the field of library and information science, this article is mainly based on literature from other disciplines. The goal was to consider how much BCI solutions can affect library information retrieval systems. The considerations presented in this article are theoretical in nature due to the lack of empirical materials on which to base. The author's assumption was to initiate a discussion about BCI on the basis of library and information science, not to propose final solutions.

The results can be widely used in practice as a framework for the implementation of BCI in libraries.

The article can help to facilitate the debate on the role of implementing new technologies in libraries.

The problem of BCI is very rarely addressed in the subject literature in the field of library and information science.

In the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.

The system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.

The effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.

The P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

This paper describes the research underpinning the development and evaluation of a brain computer interface (BCI) system designed to be suitable for domestic use by people with acquired brain injury in order to facilitate control of their home environment. The purpose of the research is to develop a robust and user‐friendly BCI system which was customisable in terms of user ability, preferences and functionality. Specifically the human interface was designed to provide consistent visual metaphors in usage, while applications change, for example, from environmental control to entertainment and communications.

The research took a user centred design approach involving representative end‐users throughout the design and evaluation process. A qualitative study adopting user interviews alongside interactive workshops highlighted the issues that needed to be addressed in the development of a user interface for such a system. User validation then underpinned prototype development.

The findings of the research indicate that while there are still significant challenges in translating working BCI systems from the research laboratories to the homes of individuals with acquired brain injuries, participants are keen to be involved in the deign and development of such systems. In its current stage of development BCI is multi‐facetted and uses complex software, which poses a significant usability challenge. This work also found that the performance of the BCI paradigm chosen was considerably better for those users with no disability than for those with acquired brain injury. Further work is required to identify how and whether this performance gap can be addressed.

The research had significant challenges in terms of managing the complexity of the hardware and software set‐up and transferring the working systems to be tested by participants in their home. Furthermore, the authors believe that the development of assistive technologies for the disabled user requires a significant additional level of personalisation and intensive support to the level normally required for non‐disabled users. Coupled with the inherent complexity of BCI, this leads to technology that does not easily offer a solution to both disabled and non‐disabled users.

The research contributes additional findings relating to the usability of BCI systems. The value of the work is to highlight the practical issues involved in translating such systems to participants where the acquired brain injury can impact on the ability of the participant to use the BCI system.

This paper aims to provide an insight into the emerging use of robots in the rehabilitation of sufferers from strokes and other neurological impediments.

This considers research, clinical trials and commercial products. Following an introduction, it explains brain neuroplasticity and its role in rehabilitation and then discusses the use of robots in the restoration of upper limb and hand movement in stroke and traumatic injury patients. Robotic techniques aimed at restoring ambulatory ability are then discussed, followed by examples of the application of brain–computer interface technology to robotic rehabilitation. Finally, concluding comments are drawn.

Research has shown that robotic techniques can assist in the restoration of functionality to partially or fully paralysed upper and lower limbs. A growing number of commercial exoskeleton and end-effector robotic products have been launched which are augmenting conventional rehabilitation therapies. These systems frequently include interactive computer games and tasks which encourage repetitive use and allow patients to monitor their progress. Trials which combine robotics with brain–computer interface technology have yielded encouraging and unexpectedly positive results.

This provides details of the increasingly important role played by robots in the rehabilitation of patients suffering from strokes and other neurological disorders.

The purpose of this paper is to provide details of recent developments in human‐robot interfacing technologies.

This paper considers recently developed or emerging technologies which allow humans to interact with robots in novel ways. It first considers inexpensive robots which are simple to programme and which can work alongside humans in a manufacturing environment. It then discusses assistive robots, which aim to help the aged or infirm and finally, the latest progress in controlling robots with the human brain is reported.

This shows that new and improved human‐robot interfacing technologies are the topic of a major development effort. Low‐cost robots that can readily be commissioned and operated in close proximity to humans are starting to impact the market. Assistive robot technology is progressing due to novel man‐machine interfacing techniques and the first instances of quadriplegic patients using their mind to control robots to manipulate object in three‐dimensional space is discussed.

This paper provides details of significant, recent developments in human‐robot interfacing.

The presented work contributes to research in the field of advanced man-machine interaction and to research in the field of formalisation and verification of complex systems. This work was motivated by the need to provide a detailed and well understandable formal description of embedded brain reading (eBR). The paper aims to discuss these issues.

The paper first introduces eBR and points out its main features. Next, a general model for eBR is developed to describe the overall architecture, integral parts and dependencies between those parts. The model is developed and presented in a formal structured form that allows for application of optimisation as well as verification techniques.

The paper demonstrates using implementations that the application of the formal model allows to check for completeness and correctness to detect errors in implementation, which were invisible without formalising eBR. In summary, the presented work contributes a formal model for a complex system and shows that such a formal model can improve the overall system's functionality.

For future work, the results support the application of formal modelling and verification techniques at the system level and the development of methods to prove for correctness and completeness of complex systems during their development.

The paper describes for the first time eBR and presents a formal model for it. It illustrates how an error-prone approach like BR can be applied safely by embedding it into the control of a real system and by applying mechanisms that control for its correct function.

The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.

This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.

This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.

The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.

This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.

This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS).

The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score.

The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score.

Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation.

This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly.

To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.

This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.

Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.

An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

The purpose of this paper is to outline the application of user centred design (UCD) within a research project to support the design, development and evaluation of a brain computer interface (BCI) with associated home-based services and remote therapy station for people with acquired brain injury (ABI).

A multi- stakeholder UCD approach was adopted to include people living with ABI, their caregivers and therapists providing rehabilitation. A three-phased iterative approach was implemented: Phase 1 was to gather user requirements, Phase 2 an iterative design phase with end user (EU) groups and therapists and finally the verification and implementation phase. The final phase had two strands of a home-based BCI evaluation with target EUs and their caregivers, alongside this, therapists evaluated the final therapist station that supports the use of the BCI at home. Ethical governance, inline with Ulster University, was awarded.

UCD enabled the co-creation and validation of a home-based BCI system for social inclusion and rehabilitation.

This was the first BCI project to adopt UCD to design and validation a novel home-based BCI system and migrate this from the lab to home. It highlights the importance of UCD to bridge the gap between the technical developers and those whom the technology is aimed at. This complex design process is essential to increase usability and reduce device abandonment.