Brain-computer Technology Research Articles

A Systematic Analysis of Wearable Devices as Brain-Computer Interface Based on Neuromodulation

As the global health landscape deteriorates, the imperative of ensuring our well-being becomes increasingly apparent. The evolution of integrated circuit miniaturization technology has propelled the advancement of brain-computer interfaces to the forefront of research. Breakthroughs in recording and stimulating deep brain activity in humans have paved the way for a groundbreaking healthcare delivery method, referred to as the "Neural Stack" – a wearable medical device leveraging neuromodulation. This innovation contributes significantly to both the development of brain-computer technology and the broader field of neuroscience. In this article, the author delve into recent milestones within the realm of brain-computer interfaces, dissecting the associated challenges and envisioning future developments and applications. Given the interdisciplinary nature of brain-computer technology, contemporary scholars in the new era are compelled to explore and comprehend its intricacies. We are on the brink of an era where all things are interconnected, with brain-computer interfaces serving as the conduit for information exchange.

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda.

This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

Exorganic Posthumanism and Brain-Computer Interface Technologies

Transhumanism is a philosophical movement that argues in favor of technologically mediated transformation of humans. Some transhumanists claim that radical human enhancement could one day create new posthuman species. Two prominent paths could lead toward posthumans. Organic posthumanism maintains the essential role of the biological in posthumans. Exorganic posthumanism reasons for synthetic, electronic and digital qualities of future posthuman beings. This article is dedicated to the examination of the exorganic road to posthumans. Brain-computer interface technologies enable direct interaction between the mind and the virtual, therefore it is typically claimed that this technology could generate exorganic posthuman adaptations. It is argued, that current brain-computer interface technologies are under similar conceptual and ethical ambiguity, as some technologies for organic posthumanism. Brain-computer technologies are consequently tools, which are neutral regarding the posthumanism goal.

Design and Experimental Research on EEG Control System of Unmanned Vehicle Based on Brain-Computer Technology

The study on brain-computer interface technology to achieve the automatic control of unmanned vehicles can help people with disabilities to realize self-service travel, thus attracting more and more attention from scholars and manufacturers. In this paper, the visual evoked potentials of human brain are extracted by visual stimulator of FPGA, and the evoked potential vector by waveform matching recognition algorithm on Labview platform, which are used as the control signals of brain-computer interface to realize automatic control of unmanned vehicle. The article explains the basis of related technologies, based on which, the signal processing flow of unmanned vehicle control system is introduced. Finally, experiment on the automatic system control of unmanned vehicle based on visual evoked potentials is designed. The experiment shows that the average time for sending instructions is less than 3s, and the average correct recognition rate of instructions is higher than 90%. The present research has opened up the research on the brain-computer interface controlled unmanned vehicle field, and will have a positive effect for the ultimate realization of autonomous travel for patients with limited mobility.