Brain-Machine Interface, also called Brain-Computer Interface (BCI), is an emerging research field with diverse applications ranging from rehabilitation to human augmentation. Thanks to the advent of BCI techniques, brain activities are no longer limited to medical diagnostics but to more applications that aim to change the user lifestyle. It acts as a direct interface between humans and computer systems, eliminating the need for muscle interventions and external devices for issuing any commands and codes. Practically speaking, many technological innovations have developed to bridge the gap between two realms, the physical and the digital world, where BCI is one such successful innovation that reads the human mind and converts the brain signal into the exact output desired by the user. This technology is poised to revolutionize the next trendy consumer device blurring the gap between the human mind and computer interfaces. In general, signal acquisition, signal processing, and effector devices are the three major components of the BCI systems. In real-time, it has varied use-cases, which can be used as an assistive device for disabled individuals and can even be used for advanced video game control.
Practically speaking, control algorithms for the critical components of the BCI algorithm. Which is often called a decoder. The use of control algorithms enables the BCI systems to interact seamlessly with the external devices. However, there is a possibility that the neural activity can change over the time, imposing substantial challenges on real time BCI implementation from a control perspective. It is most crucial to understand how the behavioural information is distributed across the multiple brain areas, and the cutting-edge interfaces are required to incorporate the models of the brain as a feedback control system. Because the efficient implementation of control algorithms will significantly find ways and leverage learning while adapting themselves to the unexpected changes in the neural code. Hence, it is more crucial to explore innovative methods for optimizing decoders for closed loop control, and more advanced control strategies for addressing neural plasticity.
Further, these applications help solve various medical issues, promise the restoration of sensory and motor function, and aid in the treatment of neurological disorders. The BCI system is directly linked to the human brain and may negatively impact if it is not used appropriately. Some potential risks include the inaccuracy of the results, the complex nature of the system, and the lack of security. However, if successfully implemented, research in this stream will go a long way in the domain of neuro aesthetics and help develop better mechanisms to protect end-users from the unnecessary consequences of BCI devices while encouraging a wide range of commercial and medical applications. Continuous progress in disruptive technologies such as artificial intelligence and control theory has prompted considerable improvements in BCI applications. Further, the more advanced research on artificial intelligence and reinforcement learning converged with cutting-edge neural interfacing technologies and signal processing techniques with control algorithms will help better understand the brain activities for developing interfaces to interact with computers and other devices.
In this special issue, we aim to explore the deeper insights on BCI, their enabling technologies and resolving the various challenges associated with it to find out its most promising applications for the near future.
Topics of interest include, but not limited to, the following:
Special Issue Schedule Timeline:
Prof. Jerry Chun-Wei Lin
Western Norway University of Applied Sciences, Norway
Prof. Gautam Srivastava
Brandon University, Canada
Prof. Yu-Dong Zhang
University of Leicester, UK
Room 524, EE Building II,
National Taiwan University,
No. 1, Sec. 4, Roosevelt Road,
Taipei City, 10617, Taiwan
(Ms.) Adeline Wu