Brain–computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms—such as steady-state visual evoked potential (SSVEP), motor imagery, and P300—and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage. These distortions have contributed to the misallocation of research resources and public misunderstanding. To foster the sustainable development of BCI, this paper advocated the establishment of a standardized TRL evaluation system, clearer terminological boundaries, stronger support for fundamental research, enhanced ethical oversight, and the implementation of inclusive and diversified governance mechanisms.
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.
Brain control is a new control method. The traditional brain-controlled robot is mainly used to control a single robot to accomplish a specific task. However, the brain-controlled multi-robot cooperation (MRC) task is a new topic to be studied. This paper presents an experimental research which received the "Innovation Creative Award" in the brain-computer interface (BCI) brain-controlled robot contest at the World Robot Contest. Two effective brain switches were set: total control brain switch and transfer switch, and BCI based steady-state visual evoked potentials (SSVEP) was adopted to navigate a humanoid robot and a mechanical arm to complete the cooperation task. Control test of 10 subjects showed that the excellent SSVEP-BCI can be used to achieve the MRC task by appropriately setting up the brain switches. This study is expected to provide inspiration for the future practical brain-controlled MRC task system.
To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.
Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.
Implantable brain-computer interfaces (BCIs) have potentially important clinical applications due to the high spatial resolution and signal-to-noise ratio of electrodes that are closer to or implanted in the cerebral cortex. However, the surgery and electrodes of implantable BCIs carry safety risks of brain tissue damage, and their medical applications face ethical challenges, with little literature to date systematically considering ethical norms for the medical applications of implantable BCIs. In order to promote the clinical translation of this type of BCI, we considered the ethics of practice for the medical application of implantable BCIs, including: reducing the risk of brain tissue damage from implantable BCI surgery and electrodes, providing patients with customized and personalized implantable BCI treatments, ensuring multidisciplinary collaboration in the clinical application of implantable BCIs, and the responsible use of implantable BCIs, among others. It is expected that this article will provide thoughts and references for the research and development of ethics of the medical application of implantable BCI.
Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combined with EEG was designed and verified by the experiment. According to the 10-20 system or 10-20 extended system, the diameter and spacing of NIRS probe and EEG electrode, NIRS probes were aligned with C3 and C4 as the reference electrodes, and NIRS probes were placed in the middle position between EEG electrodes to simultaneously measure variations of NIRS and the corresponding variation of EEG in the same functional brain area. The clamp holder and near infrared probe were coupled by tightening a screw. To verify the feasibility and effectiveness of the multi-modal EEG-NIRS helmet, NIRS and EEG signals were collected from six healthy subjects during six mental tasks involving the right hand clenching force and speed motor imagery. These signals may reflect brain activity related to hand clenching force and speed motor imagery in a certain extent. The experiment showed that the EEG-NIRS helmet designed in the paper was feasible and effective. It not only could provide support for the multi-modal motor imagery brain-computer interface based on EEG-NIRS, but also was expected to provide support for multi-modal brain functional imaging based on EEG-NIRS.
With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.
Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.
Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major—BCI major—has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.