Plasma metabolomics identifies S-adenosylmethionine as a biomarker and potential therapeutic target for vascular aging in older adult males.

Brachial-ankle pulse wave velocity (baPWV) is a noninvasive index of vascular aging. However, the metabolic profile underlying vascular aging has not yet been fully elucidated. The current study aimed to identify circulating markers of vascular aging as assessed by baPWV and to elucidate its mechanism from a metabolomic perspective in older adults. A total of 60 and 61 Chinese male participants aged ≥80 years were recruited to the metabolome and validation cohorts, respectively. The baPWV of participants was measured using an automatic waveform analyzer. Plasma metabolic profile was investigated using ultra-performance liquid chromatography coupled with triple quadrupole linear ion trap tandem mass spectrometry. Orthogonal partial least squares (OPLS) regression modeling established the association between metabolic profile and baPWV to determine important metabolites predictive of vascular aging. Additionally, an enzyme-linked immunosorbent assay was employed to validate the metabolites in plasma and culture media of vascular smooth muscle cells in vitro. OPLS modeling identified 14 and 22 metabolites inversely and positively associated with baPWV, respectively. These 36 biomarkers were significantly enriched in seven metabolite sets, especially in cysteine and methionine metabolism (p <0.05). Notably, among metabolites involved in cysteine and methionine metabolism, S-adenosylmethionine (SAM) level was inversely related to baPWV, with a significant correlation coefficient in the OPLS model (p <0.05). Furthermore, the relationship between SAM and vascular aging was reconfirmed in an independent cohort and at the cellular level in vitro. SAM was independently associated with baPWV after adjustments for clinical covariates (ß = -0.448, p <0.001) in the validation cohort. In summary, plasma metabolomics identified an inverse correlation between SAM and baPWV in older males. SAM has the potential to be a novel biomarker and therapeutic target for vascular aging.

A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding.

Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.

Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

Electromagnetic Source Imaging With a Combination of Sparse Bayesian Learning and Deep Neural Network.

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies.

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

A large-scale dataset for end-to-end table recognition in the wild.

Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial & ontology (logical) structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1 K tables in which 20.4 K are in English and 17.7 K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial & logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation.

Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method.

Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, ß and γ rhythms in lower-limb movement imagery tasks.

Electromagnetic Source Imaging via Bayesian Modeling With Smoothness in Spatial and Temporal Domains.

Accurate reconstruction of cortical activation from electroencephalography and magnetoencephalography (E/MEG) is a long-standing challenge because of the inherently ill-posed inverse problem. In this paper, a novel algorithm under the empirical Bayesian framework, source imaging with smoothness in spatial and temporal domains (SI-SST), is proposed to address this issue. In SI-SST, current sources are decomposed into the product of spatial smoothing kernel, sparseness encoding coefficients, and temporal basis functions (TBFs). Further smoothness is integrated in the temporal domain with the employment of an underlying autoregressive model. Because sparseness encoding coefficients are constructed depending on overlapped clusters over cortex in this model, we derived a novel update rule based on fixed-point criterion instead of the convexity based approach which becomes invalid in this scenario. Entire variables and hyper parameters are updated alternatively in the variational inference procedure. SI-SST was assessed by multiple metrics with both simulated and experimental datasets. In practice, SI-SST had the superior reconstruction performance in both spatial extents and temporal profiles compared to the benchmarks.

Toward Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface.

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.

Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l 2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces.

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration.

Capsule Network for ERP Detection in Brain-Computer Interface.

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.

A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.

Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification.

Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility.

Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain-Computer Interfaces.

Motor imagery (MI) decoding is an important part of brain-computer interface (BCI) research, which translates the subject's intentions into commands that external devices can execute. The traditional methods for discriminative feature extraction, such as common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP), have only focused on the energy features of the electroencephalography (EEG) and thus ignored the further exploration of temporal information. However, the temporal information of spatially filtered EEG may be critical to the performance improvement of MI decoding. In this paper, we proposed a deep learning approach termed filter-bank spatial filtering and temporal-spatial convolutional neural network (FBSF-TSCNN) for MI decoding, where the FBSF block transforms the raw EEG signals into an appropriate intermediate EEG presentation, and then the TSCNN block decodes the intermediate EEG signals. Moreover, a novel stage-wise training strategy is proposed to mitigate the difficult optimization problem of the TSCNN block in the case of insufficient training samples. Firstly, the feature extraction layers are trained by optimization of the triplet loss. Then, the classification layers are trained by optimization of the cross-entropy loss. Finally, the entire network (TSCNN) is fine-tuned by the back-propagation (BP) algorithm. Experimental evaluations on the BCI IV 2a and SMR-BCI datasets reveal that the proposed stage-wise training strategy yields significant performance improvement compared with the conventional end-to-end training strategy, and the proposed approach is comparable with the state-of-the-art method.

Ginsenoside Rd Ameliorates Auditory Cortex Injury Associated With Military Aviation Noise-Induced Hearing Loss by Activating SIRT1/PGC-1α Signaling Pathway.

Free radicals and oxidative stress play an important role in the pathogenesis of noise-induced hearing loss (NIHL). Some ginseng monomers showed certain therapeutic effects in NIHL by scavenging free radicals. Therefore, we hypothesized that ginsenoside Rd (GSRd) may exert neuroprotective effects after noise-induced auditory system damage through a mechanism involving the SIRT1/PGC-1α signaling pathway. Forty-eight guinea pigs were randomly divided into four equal groups (normal control group, noise group, experimental group that received GSRd dissolved in glycerin through an intraperitoneal injection at a dose of 30 mg/kg body weight from 5 days before noise exposure until the end of the noise exposure period, and experimental control group). Hearing levels were examined by auditory brainstem response (ABR) and distortion product otoacoustic emission (DPOAE). Hematoxylin-eosin and Nissl staining were used to examine neuron morphology. RT-qPCR and western blotting analysis were used to examine SIRT1/PGC-1α signaling and apoptosis-related genes, including Bax and Bcl-2, in the auditory cortex. Bax and Bcl-2 expression was assessed via immunohistochemistry analysis. Superoxide dismutase (SOD), malondialdehyde (MDA), and glutathione peroxidase (GSH-Px) levels were determined using a commercial testing kit. Noise exposure was found to up-regulate ABR threshold and down-regulate DPOAE amplitudes, with prominent morphologic changes and apoptosis of the auditory cortex neurons (p < 0.01). GSRd treatment restored hearing loss and remarkably alleviated morphological changes or apoptosis (p < 0.01), concomitantly increasing Bcl-2 expression and decreasing Bax expression (p < 0.05). Moreover, GSRd increased SOD and GSH-Px levels and decreased MDA levels, which alleviated oxidative stress damage and activated SIRT1/PGC-1α signaling pathway. Taken together, our findings suggest that GSRd ameliorates auditory cortex injury associated with military aviation NIHL by activating the SIRT1/PGC-1α signaling pathway, which can be an attractive pharmacological target for the development of novel drugs for NIHL treatment.

Comparison of sound location variations in free and reverberant fields: An event-related potential study.

This study compares event-related potentials (ERPs) elicited by variations of sound location in free and reverberant fields. The virtual sound sources located at azimuths 0°-40° were synthesized with head-related transfer functions and binaural room impulse responses for free and reverberant fields, respectively. The sound stimulus at 0° was assigned as standard in the oddball paradigm. Results show that the P3 amplitude is larger in the free field and acoustical conditions have no significant effect on the amplitudes of N2 and mismatch negativity. Moreover, a linear relationship between sound angle and amplitude of ERP components is observed.

Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation.

OBJECTIVE: For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. APPROACH: In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects' brain control ability online based on quantized attention-gated kernel reinforcement learning. With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator. MAIN RESULTS: The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control. SIGNIFICANCE: We discuss how our proposed method shows promise for BCI applications that can evaluate subjects' brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject's instruction and robot autonomy.

Hyperbaric oxygen treatment mitigates liver damage in mice with noise exposure.

Noise exposure relates to various pathological disorders including liver damage, preventive measures of which are being demanded. Hyperbaric oxygen treatment (HBOT), as a non-invasive procedure, exerts convincing therapeutic potency on multiple liver diseases. The efficacy of HBOT in mitigating noise induced liver damage (NILD) and associated mechanisms would be elucidated here. Mice were subject to broad band noise (20-20k Hz, 90-110 dB) for 5 days by 3 hours/day. HBOT with 2.5 atmosphere absolute (ata) was employed before noise exposure. Morphology of liver tissue was examined by hematoxylin-eosin (HE) staining. Oil Red O (ORO), transferase-mediated dUTP nick end labelling (TUNEL) test and western blot were utilized to detect lipid accumulation, apoptotic cells and protein expression, respectively. Ceramide (Cer) level was assayed by immunohistochemistry (IHC) analysis. With noise exposure, conspicuous structural derangement and lipid deposition occurred in liver tissue of mice, which was alleviated significantly by the application of HBOT. Meanwhile, HBOT reduced the proportion of apoptotic hepatocytes, restraining the superoxide production in noise exposed mice. In view of underlying mechanisms, noise enhanced the acid sphingomyelinase (ASM) protein expression and the Cer generation in liver tissue of mice which was reversed substantially by HBOT. Altogether, HBOT ameliorates the structural and functional derangement of liver by neutralizing the ASM/Cer pathway in noise exposed mice.

A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface.

To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain machine interface (BMI) is presented for all-time continuous wheelchair navigation task. Moreover, to obtain more accurate brain control commands for shared controller and adapt the proposed system to the uncertainty of electroencephalogram (EEG), a hierarchical brain control mechanism with feedback rule is designed. Experiments have been conducted to verify the proposed system in several scenarios. Eleven subjects participated in our experiments and the results illustrate the effectiveness of the proposed method.