Brain–Computer Interfaces: Novel Applications and Interactive Technologies

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Background

In the past, Brain–computer Interfaces (BCIs) were conceived as a means to enable rudimentary communication to subjects with severe neuromuscular disorders, such as patients with locked-in syndrome or amyotrophic lateral sclerosis, thus helping to improve their quality of life. The recent integration of miniaturized electronics with wireless communication technologies permits the development of multimodal and wearable technologies for neuro-sensing as well as for the reproduction of real-and-virtual combination of environments and human-machine interactions such as in the case of the Extended Reality (XR).

While in the past BCIs were intended for human-computer interaction, now the combination of XR and/or with brain-based technologies opens the door for more sophisticated ways of interaction such as human-to-human, or human-to-object with a direct application in neuro-rehabilitation therapies. For instance, Virtual Reality (VR) has been used for rehabilitation of cognitive and motor impairment in people with dementia by means of reminiscence therapies. Furthermore, recent works show that the combination of conventional therapies with VR and brain-based technologies (e.g., BCIs, functional electrical stimulation (FES), and neuro-feedback) for motor impairment rehabilitation can be more effective than conventional means alone. In summary, the main conclusion after the analysis of studies of the last years is that VR as well as brain-state dependent protocols can be considered effective tools to improve cognitive function.

In the future, we expect the deployment of ensembles of implantable, bidirectional and miniaturized neuro-sensors with enhanced communication capabilities to constitute a novel and challenging paradigm, the Wireless Brain Area Network (WBRAN). This multidisciplinary paradigm will encompass communication protocols, energy harvesting, neuro-signal acquisition and processing, and application of advanced machine learning theory and methods to data. WBRAN will support the Internet-based computing of multiple sources of information, such as from the cerebral activity (provided by brain–based technologies), from the behavior (provided by XR technologies) and from the assisted ambient (provided by smart ambient sensors and the IoT). This exciting horizon of interactive brain-based technology opens the door for new opportunistic applications, such as in the fields of healthcare, telemedicine, assisted living, education, entertainment, culture, marketing and others.

This Research Topic explores the advances, challenges, and future prospects of the BCI technology applied in interactive scenarios and possibly enriched by XR:

- Invasive and non-invasive brain–computer interfaces, innovative multimodal headsets, integrated stimulation-acquisition devices
- Closed-loop applications: Design and novel paradigms of neurometry and sensory stimulation applied in any field. It includes specific goals such as cognitive workload assessment, emotion recognition, neuro-security, attention detection, moto- and cognitive rehabilitation, among others
- Pervasive use of Brain-based technology in combination with XR (and related peripherals such as eye-trackers, haptic gloves, cyber shoes, force suits and others) without using verbal commands
- Scalable, possibly internet-based neuro-signals processing algorithms for very large numbers of neuro-sensors, and advanced machine/deep learning methods
- Massive implantable neuro-sensors: wireless communication protocols, efficient energy harvesting, architecture and design

Dr. Christoph Guger works for G.Tec. G.Tec is a MSE that develops hardware/software for EEG and other biosignals analysis. The other Topic Editors declare no competing interests with regard to the Research Topic subject

Keywords: Brain-computer interfaces, Brain-machine interface, Virtual Reality, Augmented Reality, Neurometry, Affective computing, Human-machine interaction, Biomedical signal processing, Neuro-prostheses, Wireless Brain Area Networks

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