Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.
Brain-Computer Interfaces ; United States ; United States Food and Drug Administration ; Humans ; Electroencephalography

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.
Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Imagery, Psychotherapy/methods*

With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.
Humans ; Brain-Computer Interfaces ; Consciousness Disorders/physiopathology* ; Electroencephalography ; Brain/physiopathology* ; Consciousness

Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks ( P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.
Humans ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods* ; Electroencephalography/methods* ; Consciousness Disorders/diagnosis* ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/diagnosis*

This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male

Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male

Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.
Humans ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Neural Networks, Computer ; Speech/physiology* ; Algorithms ; Male ; Adult ; Imagination

Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.
Humans ; Brain-Computer Interfaces ; Stroke Rehabilitation ; Upper Extremity/physiopathology* ; Brain/physiopathology* ; Electroencephalography ; Stroke/physiopathology*