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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.

The purpose of this paper is to provide a technical insight into recent developments in brain‐computer interface (BCI) technology and its applications.

Following an introduction to BCI, this paper considers the different means of detecting brain waves and then discusses a number of BCI development programmes and applications.

Invasive, partially invasive and non‐invasive BCI techniques are the topic of extensive study and aim to allow the control of external devices by human thought. Invasive BCIs are being applied to patients suffering paralysis and requiring cranial surgery and aim to restore movement or impart other functions such as the operation of robotic arms or communication. Wireless invasive microelectrode arrays are under development. Of the non‐invasive techniques, electroencephalography is attracting the greatest interest and is being applied to a range of military and healthcare uses.

This paper provides an introduction to BCI technology and a review of recent research and a number of key applications.

A research framework that explains adoption intention in students with regard to brain–computer interface (BCI) games in the learning context was proposed and empirically examined.

In this study, an approach integrating the decomposed theory of planned behavior, perceived playfulness, risk and the task–technology fit (TTF) concept was used to assess data collected using a post-experiment questionnaire from a student sample in Taiwan. The research model was tested using the partial least-squares structural equation modeling (PLS-SEM) technique.

Attitude, subjective norms and TTF were shown to impact intention to play the BCI game significantly, while perceived behavioral control did not show a significant impact. The influence of superiors and peers was found to positively predict subjective norms. With the exception of perceived ease of use, all of the proposed antecedents were found to impact attitude toward BCI games. Technology facilitating conditions and BCI technology characteristics were shown to positively determine perceived behavior control and TTF, respectively. However, the other proposed factors did not significantly influence the latter two dependents.

This research contributes to the nascent literature on BCI games in the context of learning by highlighting the influence of belief-related psychological factors on user acceptance of BCI games. Moreover, this study highlights the important, respective influences of perceived playfulness, risk and TTF on users' perceptions of a game, body monitoring and technology implementation, each of which is known to influence willingness to play.

Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.

To survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.

For the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.

Based on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

The paper aims to describe the sensors used for interfacing with consumer electronic devices.

The paper describes the types of sensors employed in user‐interface devices such as trackballs, mice, touch pads, touch screens and gesture‐based systems. It concludes with a brief consideration of brain‐computer interface technology.

It is shown that a diverse range of sensors is used to interface with consumer electronics. They are based on optical, electrical, acoustic and solid‐state (MEMS) technologies. In the longer term, many may ultimately be replaced by sensors that interpret thought by detecting brain waves.

The paper provides a timely review of the sensors used to interface with consumer electronics. These constitute a very large and rapidly growing market.

This paper aims to provide details of recent advances in robotic prostheses with the emphasis on the control and sensing technologies.

Following a short introduction, this paper first discusses the main robotic prosthesis control strategies. It then provides details of recent research and developments using non-invasive and invasive brain–computer interfaces (BCIs). These are followed by examples of studies that seek to confer robotic prostheses with sensory feedback. Finally, brief conclusions are drawn.

A significant body of research is underway involving electromyographic and BCI technologies, often in combination with advanced data processing and analysis schemes. This has the potential to yield robotic prostheses with advanced capabilities such as greater dexterity and sensory feedback.

This illustrates how electromyographic, BCI, signal processing and sensor technologies are being used to create robotic prostheses with enhanced functionality.

To examine the effects of the metaverse on firms’ marketing activities.

A conceptual paper.

It provides evidence of the growing importance of different value capture mechanisms in the metaverse.

Among the first articles on this topic.

This work proposes classification of two-class motor imagery electroencephalogram signals using different automated machine learning algorithms. Here data are decomposed into various frequency bands identified by wavelet transform and will span the range of 0–30 Hz.

Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train the classifiers. Further, the assessment parameters such as SNR, mean, SD and entropy are calculated to analyze the performance of the proposed work.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.