A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Further, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e. simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specifically, the steady-state motion visual evoked potentials (SSMVEP) paradigm, which has been shown to induce less visual discomfort, was chosen as the experimental paradigm. Two processing pipelines were proposed for the detection of IC and NC states. The first one was using CNN as a multi-class classifier to discriminate between all the states in IC and NC state (FFT-CNN). The second one was using CNN to discriminate between IC and NC states, and using canonical correlation analysis (CCA) to perform classification tasks within the IC (FFT-CNN-CCA). We demonstrated that both pipelines achieved a significant increase in accuracy for low-performance healthy participants when traditional algorithms such as CCA threshold were used. Further, the FFT-CNN-CCA pipeline achieved better performance than the FFT-CNN pipeline based on the stroke patients’ data. In summary, we showed that CNN can be used for robust detection in an asynchronous SSMVEP-BCI with great potential for out-of-lab BCI applications.

With the advent of an aging society, stroke makes a heavy burden for our society. Stroke can damage the motor and sensory neural system and block the closed loop between the brain and the body. Due to the neural plasticity, this closed loop can be rebuilt through training. Users' actively engagement can help expedite functional recovery. Therefore, we propose an EEG-driven Lower Limb Rehabilitation Training System (LLRTS) that can achieve Active and Passive Co-stimulation (APC). Virtual Reality (VR), BCI and robot are introduced into the system. When users have motor intentions, this system could automatically support corresponding visual and somatic sensory feedback. That is to say active and passive stimulations are controlled by user's mind. This paper reports the idea and construction of this rehabilitation system. Preliminary experimental results support the concept.

Individuals with mild cognitive impairment (MCI), the preclinical stage of Alzheimer disease (AD), suffer decline in their visual working memory (WM) functions. Using large-scale network analysis of electroencephalography (EEG), the current study intended to investigate if there are differences in functional connectivity properties extracted during visual WM coding stages between MCI patients and normal controls (NC). A total of 21 MCI patients and 20 NC performed visual memory tasks of load four, while 32-channel EEG recordings were acquired. The functional connectivity properties were extracted from the acquired EEGs by the directed transform function (DTF) via spectral Granger causal analysis. Brain network analyses revealed distinctive brain network patterns between the two groups during the WM coding stage. Compared with the NC, MCI patients exhibited a reduced visual network connectivity of the frontal-temporal in θ (4-7Hz) band. A likely compensation mechanism was observed in MCI patients, with a strong brain functional connectivity of the frontal-occipital and parietal-occipital in both θ and α (8-13Hz) band. Further analyses of the network core node properties based on the differential brain network showed that, in θ band, there was a significant difference in the out-degree of the frontal lobe and parietal lobe between the two groups, while in α band, such difference was located only in the parietal lobe. The current study found that, in MCI patients, dysconnectivity is found from the prefrontal lobe to bilateral temporal lobes, leading to increased recruitment of functional connectivity in the frontal-occipital and parietal-occipital direction. The dysconnectivity pattern of MCI is more complex and primarily driven by core nodes Pz and Fz. These results significantly expanded previous knowledge of MCI patients’ EEG dynamics during WM tasks and provide new insights into the underpinning neural mechanism MCI. It further provided a potential therapeutic target for clinical interventions of the condition.

Many of current human pose estimation methods based on depth images require training stage. However, the training stage costs huge work on making samples. And many methods for human pose occlusion condition cannot work well. In this study, a novel approach to estimate human pose with a depth image called model‐based recursive matching (MRM) is introduced. A human skeleton model with customised parameters is created based on T‐pose to fit different body types. The authors use depth image and 3D point cloud corresponding to input. In contrast to previous work, the proposed method avoids training step and can give an accurate estimation in the case of the human occlusion condition. They demonstrate the method by comparing to the method Kinect offered by using random forest on 20 human poses. And the ground truth of coordinates of pose joint is made by the motion capture system. The result shows that the proposed method not only works well on the general human pose but also can deal with human occlusion better. And the authors’ method can be also applied to the disabled people and other creatures.

Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient’ motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)–based BCI was proposed. The visual stimuli of four types of hand movements were designed to simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital region and to activate the sensorimotor region. Task-related component analysis was performed to identify the SSMVEP. Results showed that the amplitude of the induced frequency in the SSMVEP had a negative relationship with the stimulus frequency. The classification accuracy in the four-class scenario reached 72.81 ± 13.55% within 2.5s. Importantly, the AO-based closed-loop BCI, which provided visual feedback based on the SSMVEP, could enhance ERD compared with AO-alone. The increased attentiveness might be one key factor for the enhancement of the ERD in the designed AO-based BCI. In summary, the proposed AO-based BCI provides a new insight for BCI-based rehabilitation.

Objective. Different visual stimuli might have different effects on the brain, e.g. the change of brightness, non-biological movement and biological movement. Approach. In this study, flicker, checkerboard and gaiting stimuli were chosen as visual stimuli to investigate whether steady-state motion visual evoked potential (SSMVEP) effect on the sensorimotor area for rehabilitation. The gaiting stimulus was designed as the gaiting sequence of a human. The hypothesis is that only observing the designed gaiting stimulus would simultaneously induce: (1) SSMVEP in the occipital area, similarly to an SSVEP stimulus; and (2) sensorimotor rhythm (SMR) in the primary sensorimotor area, because such action observation could activate the mirror neuron system. Canonical correlation analysis was used to detect SSMVEP from occipital electroencephalograms (EEG), and event-related spectral perturbation was used to identify SMR in the EEG from the sensorimotor area. Main results. The results showed that the designed gaiting stimulus-induced SSMVEP, with classification accuracies of 88.9 ± 12.0% in a four-class scenario. More importantly, it induced clear and sustained event-related desynchronization/synchronization (ERD/ERS), while no ERD/ERS could be observed when the other two SSVEP stimuli were used. Further, for participants with a sufficiently clear SSMVEP pattern (classification accuracy >85%), the ERD index values in the mu-beta band induced by the proposed gaiting stimulus were statistically different from those of the other two types of stimulus. Significance. Therefore, a novel brain–computer interface (BCI) based on the designed stimulus has potential in neurorehabilitation applications because it simultaneously has the high accuracy of an SSMVEP (sim90% accuracy in a four-class setup) and the ability to activate the sensorimotor area.