Effect of brain-computer interface based on visual, auditory and motor feedback combined with transcranial direct current stimulation on upper limb function in stroke patients / 中国康复理论与实践

ObjectiveTo explore the effect of brain-computer interface (BCI) based on visual, auditory and motor feedback combined with transcranial direct current stimulation (tDCS) on upper limb function in stroke patients. MethodsFrom March to October, 2023, 45 stroke inpatients in Xuzhou Rehabilitation Hospital and Xuzhou Central Hospital were divided into BCI group (n = 15), tDCS group (n = 15) and combined group (n = 15) randomly. All the groups received routine rehabilitation, while BCI group received BCI training, tDCS group received tDCS, while the combined group received tDCS and followed by BCI training immediately, for four weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE), Action Research Arm Test (ARAT), modified Barthel Index (MBI) and delta-alpha ratio (DAR) and power ratio index (PRI) of electroencephalogram before and after treatment. ResultsThe scores of FMA-UE, ARAT and MBI increased in all the groups after treatment (|t| > 5.350, P < 0.001), and all these indexes were the best in the combined group (F > 3.366, P < 0.05); while DAR and PRI decreased in all the groups (|t| > 2.208 , P < 0.05), they were the best in the combined group (F > 5.224, P < 0.01). ConclusionBCI based on visual, auditory and motor feedback combined with tDCS can further improve the motor function of upper limbs and the activities of daily living of stroke patients.

Methods for Improving Brain-Computer Interface: Using A Brain-Directed Adjuvant and A Second-Generation Artificial Intelligence System to Enhance Information Streaming and Effectiveness of Stimuli

Background: The brain-computer interface (BCI) is gaining much attention to treat neurological disorders and improve brain-dependent functions. Significant achievements over the last decade have focused on engineering and computation technology to enhance the recording of signals and the generation of output stimuli. Nevertheless, many challenges remain for the translation of BCIs to clinical applications. Methods: We review the relevant data on the four significant gaps in enhancing BCI's clinical implementation and effectiveness. Results: The paper describes three methods to bridge the current gaps in the clinical application of BCI. The first is using a brain-directed adjuvant with a high safety profile, which can improve the accuracy of brain signaling, summing of information, and production of stimuli. The second is implementing a second-generation artificial intelligence system that is outcome-oriented for improving data streaming, recording individualized brain-variability patterns into the algorithm, and improving closed-loop learning at the level of the brain and with the target organ. The system overcomes the compensatory mechanisms that underlie the loss of stimuli' effectiveness for ensuring sustainable effects. Finally, we use inherent brain parameters relevant to consciousness and brain function to bridge some of the described gaps. Conclusions: Combined with the currently developed techniques for enhancing effectiveness and ensuring a sustainable response, these methods can potentially improve the clinical outcome of BCI techniques.

Research progress of brain-computer interface application paradigms based on rapid serial visual presentation / 生物医学工程学杂志

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern / 生物医学工程学杂志

Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.

A design and evaluation of wearable p300 brain-computer interface system based on Hololens2 / 生物医学工程学杂志

Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.

Recognition of high-frequency steady-state visual evoked potential for brain-computer interface / 生物医学工程学杂志

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.

Effect of brain-computer interface training based on motor imagery on hand function for subacute stroke patients / 中国康复理论与实践

ObjectiveTo observe the effect of brain-computer interface (BCI) training based on motor imagery on hand function in hemiplegic patients with subacute stroke. MethodsFrom June, 2020 to December, 2021, 40 patients with hemiplegia in subacute stroke from Department of Rehabilitation Medicine, Fifth Affiliated Hospital of Zhengzhou University were divided into control group (n = 20) and experimental group (n = 20) using random number table. Both groups accepted medication and routine comprehensive rehabilitation, while the control group accepted hand rehabilitation robot training, and the experimental group accepted the robot training using motor imagery-based BCI, for four weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE), modified Barthel Index, modified Ashworth scale, and measured integrated electromyogram of the superficial finger flexors, finger extensors and short thumb extensors of the affected forearm during maximum isometric voluntary contraction with surface electromyography. ResultsTwo patients in the control group and one in the experimental group dropped off. All the indexes improved in both groups after treatment (t > 2.322, Z > 2.631, P < 0.05), and they were better in the experimental group than in the control group (t > 2.227, Z > 2.078, P < 0.05), except the FMA-UE score of wrist. ConclusionMotor imagery-based BCI training is more effective on hand function and activities of daily living in hemiplegic patients with subacute stroke.

Advances in brain-computer interface based on high-frequency steady-state visual evoked potential / 生物医学工程学杂志

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Roadmap of Medical Device for Implanted Brain-computer Interface / 中国医疗器械杂志

Implanted brain-computer interface (iBCI) is a system that establishes a direct communication channel between human brain and computer or an external devices by implanted neural electrode. Because of the good functional extensibility, iBCI devices as a platform technology have the potential to bring benefit to people with nervous system disease and progress rapidly from fundamental neuroscience discoveries to translational applications and market access. In this report, the industrialization process of implanted neural regulation medical devices is reviewed, and the translational pathway of iBCI in clinical application is proposed. However, the Food and Drug Administration (FDA) regulations and guidances for iBCI were expounded as a breakthrough medical device. Furthermore, several iBCI products in the process of applying for medical device registration certificate were briefly introduced and compared recently. Due to the complexity of iBCI in clinical application, the translational applications and industrialization of iBCI as a medical device need the closely cooperation between regulatory departments, companies, universities, institutes and hospitals in the future.

Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement / 生物医学工程学杂志

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.

Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials / 生物医学工程学杂志

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Ethics considerations on brain-computer interface technology / 生物医学工程学杂志

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.

Brain-computer interface coding method based on high-frequency dual-frequency SSVEP / 国际生物医学工程杂志

Objective:To improve the users’ comfort of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) through high-frequency stimulation and overcome the problem of accuracy decline caused by high frequency by combining dual-frequency encoding.Methods:Two dual-frequency high-frequency 60-instruction paradigms based on left and right visual fields and checkerboard stimuli were designed based on the 25.5 - 39.6 Hz frequency. Thirteen subjects participated in the experiment, and spectrum and spatial characteristics analyses were performed on SSVEP signals. The filter bank parameters were optimized based on the spectrum characteristics. Extended canonical correlation analysis (eCCA), ensemble task-related component analysis (eTRCA), and task-discriminant component analysis (TDCA) were used for SSVEP recognition.Results:Stable SSVEP was successfully induced in both the left and right visual fields and the checkerboard grid paradigm. The left and right visual fields had high signal-to-noise ratios for the fundamental frequency and its harmonics and weak signal-to-noise ratios for intermodulation components, whereas the intermodulation components of the 2 stimulus frequencies of the checkerboard grid, f1 + f2, had significantly higher signal-to-noise ratios than the second harmonic components above 30 Hz, and there was also a f2 ? f1 component and a 2 f1 ? f2 component. Combined with brain topography, it can be seen that the f1 and f2 response components of the left and right visual fields are located on opposite sides of the visual field, while the checkerboard grids are both concentrated in the center of the occipital region. Regarding the lateralization of brain topography amplitude and signal-to-noise ratio, the mean values of the PO3 and PO4 signal-to-noise ratios at the stimulation frequency of the left and right visual fields are consistent with the contralateral response characteristics. The 5 fb ? 1 method is the optimal filter set setting method, and the recognition correctness rate of TDCA for the left and right visual fields is the highest. However, the comparison of the recognition correctness rate of tessellated lattice eTRCA and TDCA is not statistically significant ( P > 0.05). The information transmission rates of the three algorithms all increase and then decrease with the increase in data length. Conclusions:The designed dual-frequency, high-frequency SSVEP-BCI paradigm is able to better balance performance and comfort and provides a basis for practical large instruction set BCI design methods.

Brain-computer interface technology for stroke in the past decade: a visualized analysis / 中国康复理论与实践

ObjectiveTo conduct a visualized analysis of the research related to the use of brain-computer interface technology for stroke rehabilitation in the past ten years, and identify and predict the hot spots and hot trends in order to promote the further development of this field. MethodsThe Web of Science Core Collection database was searched for literature related to brain-computer interface technology for stroke rehabilitation from January, 2011 to October, 2022. CiteSpace 5.8.R3 was used to analyze the number of publications, countries, institutions, authors, keywords, co-citations, and grant support. Results and ConclusionA total of 592 papers were included, and the annual number of publications in this field of research showed a rapid growth trend, and the research enthusiasm continued to increase. The United States was in the leading position in this field, with the highest number of cooperative publications and the highest intermediary centrality; China had certain advantages in this field, but still needed to strengthen the exchange and cooperation with other countries/regions. Foreign institutions and authors had formed a network of close cooperative relationships, and formed a high-impact team represented by Niels Birbaumer, Cuntai Guan, Kai Keng Ang, etc.; there were poor cooperative relationships among domestic authors and institutions, and there were geographical restrictions and lack of high-impact academic groups. The keywords "motor imagery" and "recovery" formed ten major clusters and 15 prominent words with high variation rates, showing a trend of diversification in research directions. The study of the efficacy of upper limb motor rehabilitation and central mechanisms has been the hot topics in this field and will continue for some time in the future; the use of lower limb brain-computer interface systems for improving foot drop, gait and balance in stroke patients and the application of multimodal brain-computer interfaces will probably become a hot topic in the future. Finally, the use of brain-computer interface-guided neurofeedback training for cognitive and language rehabilitation in stroke also needs attention.

Effect of visual motion-induced brain computer interface technology on upper limb motor and cognitive function of patients with stroke / 中国康复理论与实践

ObjectiveTo investigate the effects of visual motion-induced brain-computer interface (BCI) technology on upper limb motor function and cognitive function of patients with stroke. MethodsFrom July, 2021 to March, 2022, 50 stroke patients with upper limb hand dysfunction in Shaanxi Provincial Rehabilitation Hospital were randomly divided into control group (n = 25) and experimental group (n = 25). Both groups received conventional rehabilitation therapy, in addition, the control group received passive rehabilitation training, and the experimental group received visual motion-induced BCI rehabilitation training, for two weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE), modified Barthel Index (MBI) and Montreal Cognitive Assessment (MoCA) before and after treatment. Brain participation was evaluated during the whole training process of the experimental group. ResultsBefore treatment, there was no difference in the scores of FMA-UE, MBI and MoCA between two groups (P > 0.05). Two weeks after treatment, the scores of FMA-UE, MBI and MoCA improved in both groups (t > 2.481, P < 0.001), and were better in the exprimental group than in the control group (t > 2.453, P < 0.05); the mean brain participation of the experimental group increased 21% after treatment. ConclusionVisual motion-induced BCI rehabilitation training could promote the recovery of motor function of upper limb, and cognitive function of patients with stroke.

Design and preliminary application of outdoor flying pigeon-robot / 生物医学工程学杂志

Control at beyond-visual ranges is of great significance to animal-robots with wide range motion capability. For pigeon-robots, such control can be done by the way of onboard preprogram, but not constitute a closed-loop yet. This study designed a new control system for pigeon-robots, which integrated the function of trajectory monitoring to that of brain stimulation. It achieved the closed-loop control in turning or circling by estimating pigeons' flight state instantaneously and the corresponding logical regulation. The stimulation targets located at the formation reticularis medialis mesencephali (FRM) in the left and right brain, for the purposes of left- and right-turn control, respectively. The stimulus was characterized by the waveform mimicking the nerve cell membrane potential, and was activated intermittently. The wearable control unit weighted 11.8 g totally. The results showed a 90% success rate by the closed-loop control in pigeon-robots. It was convenient to obtain the wing shape during flight maneuver, by equipping a pigeon-robot with a vivo camera. It was also feasible to regulate the evolution of pigeon flocks by the pigeon-robots at different hierarchical level. All of these lay the groundwork for the application of pigeon-robots in scientific researches.

Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation / 生物医学工程学杂志

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.

Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis / 生物医学工程学杂志

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.

Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network / 生物医学工程学杂志

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention / 生物医学工程学杂志

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.