About this Research Topic

Background

In the past decade, considerable Industrial developments have been occurred in computerized controls and monitoring applications and these developments have further promoted the breakthrough of advanced technologies such as Brain Computer Interfaces (BCIs) augmented with Artificial Intelligence (AI). Modern BCIs lie at the intersection and integration of data acquisition, signal processing, AI, and Cyber physical systems (CPS). Breakthroughs in algorithms represented by cognitive computing stimulate the continuous penetration of AI into fields such as BCI, industry 4.0, surgery 4.0 (healthcare), an effort to build up an Industrial AI eco system. Industry 4.0 is a fast-evolving sector that aims to reform traditional industrial methods by deploying digital tools like AI and BCI. Advanced AI algorithms such as machine and deep learning can aid in improving BCI system’s performance and in achieving better outcomes, thereby supporting to deal with BCI real-life challenges more effectively. BCI-based solutions are also drawing increasing attention to support industrial performance from precise assessment to the optimization of neuroergonomic systems with accurate valuation of mental and cognitive workload of industrial operators, facilitating human-robot interactions, robotic-assisted surgeries and make operations in critical conditions more secure and safe.

BCIs provide a methodology for manipulating computers and external mechatronic devices to operate based on the brain signals and recently modern industrial sector has shown a rising interest in BCI operated machines. The research and development of cutting-edge BCI together with the development of AI may eventually usher in a strong AI focused Industry. Recent studies also suggests that BCI might be equipped to be used outside laboratories and in the close ecological settings. Various challenges are encountered by BCI systems, when exploited in real-world ecological applications. These challenges may include lack of methods for recognizing accurate human mental states and emotions further adds to complexities in BCI systems employed for emotion detection, mental workload, and mental condition recognition. Here, sophisticated approaches based on novel machine or deep learning models may be required to advance the research field by overcoming these issues. In modern Industry and health sector, efforts are moving in directions to develops hardware, software, machines, and devices with human-type intelligence.

In this issue, we shall try to share ideas, approaches, opinions, and comments regarding the latest research in field of AI and BCI and challenges relevant to the future deployment of Intelligent AI based BCI applications for Industry 4.0. Moreover, this special issue also aims at providing a forum for researchers to investigate the role of these AI methods in enhancing the performance of existing BCI applications.

Topics of interest and topic areas include but are not limited / restricted to:
Brain computer interface (BCI) / Brain–machine interface (BMI)
Intelligent brain signal processing
Artificial Intelligence (machine learning, sparse representations, deep learning, transfer learning, multimodal information fusion) applied in Bio-signals
Reinforcement learning in BCI
Data augmentation and explainable Artificial Intelligence (AI)
Industrial Brain–computer interface (BCI)
Novel BCI paradigms in industry 4.0
BCI for clinical applications
Neurorobotics and BCI
Industrial Robotics and BCI
BCI control of instruments and tools
Affective BCI in Emotion recognition, mental state recognition
Neuroergonomics and mutual learning in human-machine interaction
User needs in human-machine interaction in industry 4.0
Related applications

We welcome the submission of all accepted article types: including original research, reviews and mini-reviews, editorials, commentaries, study protocols and case reports, that illuminate broad research on Brain computer Interface (BCI) and Artificial Intelligence (AI), their applications in areas like healthcare, surgery 4.0, Industry 4.0 and Human Machine Interaction (HMI) etc.

Keywords: artificial intelligence, brain computer interface, industry, human machine interaction, Neuroergonomics, engineering psychology, cognitive neuroscience, neural technology, neurorobotics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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