The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.
Humans ; Brain-Computer Interfaces ; Technology ; Brain ; User-Computer Interface ; Electroencephalography

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Brain/physiology* ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Magnetoencephalography ; Technology

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/physiology* ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Technology ; User-Computer Interface

Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.
Neurofeedback/methods* ; Spectroscopy, Near-Infrared/methods* ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging ; Technology

Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
Brain ; Computers ; Humans ; Imagery, Psychotherapy ; Speech ; Technology

Neurofeedback training (NFT) is an important neuromodulation method that can produce certain plasticity in the central nervous system, and is expected to be an effective physical intervention method for rehabilitation of mental disorders. So far, there have been many studies on NFT promoting the rehabilitation of common mental disorders in the literature. For different mental disorders, there are different NFT training programs with different intervention effects and mechanisms. This paper focuses on the application of NFT in the rehabilitation of mental disorders, introduces the concept, process and common solutions of NFT, and focuses on the methods and applications of NFT to promote the rehabilitation of common mental disorders, and finally discusses the faced problems and future research trends, aiming to provide references for innovative research and clinical application of NFT to promote the rehabilitation of mental disorders.

Neurofeedback training (NFT) is an important neuromodulation method that can produce certain plasticity in the central nervous system, and is expected to be an effective physical intervention method for rehabilitation of mental disorders. So far, there have been many studies on NFT promoting the rehabilitation of common mental disorders in the literature. For different mental disorders, there are different NFT training programs with different intervention effects and mechanisms. This paper focuses on the application of NFT in the rehabilitation of mental disorders, introduces the concept, process and common solutions of NFT, and focuses on the methods and applications of NFT to promote the rehabilitation of common mental disorders, and finally discusses the faced problems and future research trends, aiming to provide references for innovative research and clinical application of NFT to promote the rehabilitation of mental disorders.

Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.
Brain ; Brain-Computer Interfaces ; Electroencephalography ; Ergonomics ; Humans ; User-Computer Interface

Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects' MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system's performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination

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 ; diagnostic imaging ; Brain-Computer Interfaces ; Foot ; Humans ; Imagination ; Movement ; Spectroscopy, Near-Infrared