Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R' could reach to 0.811. It can meet the daily applicatioi accuracy of mental fatigue prediction.
Electroencephalography ; Humans ; Mental Fatigue ; Models, Biological ; Sleep Deprivation

Objective To investigate the effect of tripterygium glycosides (TG) contained serurn on the pathological boneforming related inflammatory markers and miR-21.Methods Previous isolated and cultured AS fibroblasts were stimulated using IL-1 of 1ng/ml for 24h,different concentrations of blank serum (5％,10％,and 15％) and TG contained serum (5％,10％,and 15％) were added for 48h.PGE-2,IL-17,IL-22,IL-23,CCL19 and CCL21 proteins were examined by Western blot.The osteogenesis marker BMP and microRNA-21 mRNAs were tested.Results 48 h after intervention,the expressions of inflammatory markers were obviously inhibited by TG contained serum;the boncforming related inflammatory markers,expressions of BMP-2 and miR-2t were all inhibited in a dose-dependent manner.Conclusions TG could inhibit the expressions of boneforming related inflammatory markers,BMP-2 and miR-21,thus providing theoretical basis to treat AS pathological boneforming.

Selective attention promotes the perception of brain to outside world and coordinates the allocation of limited brain resources. It is a cognitive process which relies on the neural activities of attention-related brain network. As one of the important forms of brain activities, neural oscillations are closely related to selective attention. In recent years, the relationship between selective attention and neural oscillations has become a hot issue. The new method that using external rhythmic stimuli to influence neural oscillations, i.e., neural entrainment, provides a promising approach to investigate the relationship between selective attention and neural oscillations. Moreover, it provides a new method to diagnose and even to treat the attention dysfunction. This paper reviewed the research status on the relationship between selective attention and neural oscillations, and focused on the application prospects of neural entrainment in revealing this relationship and diagnosing, even treating the attention dysfunction.
Attention ; Brain ; physiology ; Humans ; Neurons ; physiology

Neurological damage caused by stroke is one of the main causes of motor dysfunction in patients, which brings great spiritual and economic burdens for society and families. Motor imagery is an important assisting method for the rehabilitation of patients after stroke, which is easy to learn with low cost and has great significance in improving the motor function and the quality of patient's life. This paper mainly summarizes the positive effects of motor imagery on post-stroke rehabilitation, outlines the physiological performance and theoretical model of motor imagery, the influencing factors of motor imagery, the scoring criteria of motor imagery and analyzes the shortcomings such as the few kinds of experimental subject, the subjective evaluation method and the low resolution of the experimental equipment in the process of rehabilitation of motor function in post-stroke patients. It is hopeful that patients with stroke will be more scientifically and effectively using motor imagery therapy.

Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human's performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person's attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
Algorithms ; Attention ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation

Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
Algorithms ; Brain ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Support Vector Machine

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Photic Stimulation

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.
Humans ; Evoked Potentials, Visual ; Brain-Computer Interfaces ; Healthy Volunteers ; Signal-To-Noise Ratio

Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Machine Learning

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.
Humans ; Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms