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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.
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Humanos , Interfaces Cerebro-Computador , Potenciales Evocados Visuales , Algoritmos , Análisis Discriminante , ElectroencefalografíaRESUMEN
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.
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Humanos , Interfaces Cerebro-Computador , Electroencefalografía/métodos , Encéfalo/fisiología , Inteligencia Artificial , Estimulación Luminosa/métodosRESUMEN
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.
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Humanos , Interfaces Cerebro-Computador , Potenciales Evocados Visuales , AlgoritmosRESUMEN
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.
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Humanos , Potenciales Evocados Visuales , Interfaces Cerebro-Computador , Voluntarios Sanos , Relación Señal-RuidoRESUMEN
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.
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Algoritmos , Interfaces Cerebro-Computador , Electroencefalografía , Potenciales Evocados Visuales , Estimulación LuminosaRESUMEN
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.
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Algoritmos , Encéfalo , Interfaces Cerebro-Computador , Análisis Discriminante , Electroencefalografía , Máquina de Vectores de SoporteRESUMEN
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.
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Algoritmos , Interfaces Cerebro-Computador , Electroencefalografía , Imágenes en Psicoterapia , Imaginación , Aprendizaje AutomáticoRESUMEN
Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain-computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLC
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Humanos , Estimulación Eléctrica , Terapia por Estimulación Eléctrica , Electroencefalografía , Recuperación de la Función , Accidente Cerebrovascular/terapia , Rehabilitación de Accidente CerebrovascularRESUMEN
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.
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Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
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Humanos , Algoritmos , Encéfalo , Fisiología , Interfaces Cerebro-Computador , Electroencefalografía , Reconocimiento de Normas Patrones AutomatizadasRESUMEN
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.
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Humanos , Algoritmos , Atención , Electroencefalografía , Potenciales Evocados Visuales , Estimulación LuminosaRESUMEN
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.
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Humanos , Atención , Encéfalo , Fisiología , Neuronas , FisiologíaRESUMEN
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.
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Vigilance refers to the brain alertness to objective things, including the concentration of attention and the capability to response emergencies. It is of great importance to study vigilance monitoring to avoid accidents caused by decrease of vigilance. In this study, traditional Mackworth Clock Test (MCT) was modified to induce decline of attended and unattended vigilance. We analyzed EEG features in different levels of attended vigilance and assessed unattended vigilance by amplitude of mismatch negative (MMN) which can be evoked by audio odd ball stimulations. The results show that with attended vigilance going down, power of theta and alpha bands of electroencephalogram (EEG) increases and Lempel-Ziv Complexities (LZC) show an undulated decline tendency. And with unattended vigilance going down, amplitude of MMN reduces significantly. This research has great reference value for the further vigilance study.
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Atención/fisiología , Electroencefalografía/métodos , Vigilia/fisiología , Adulto , Conducta , Encéfalo/fisiología , Mapeo Encefálico , Femenino , Humanos , Masculino , Adulto JovenRESUMEN
Electroencephalographic (EEG)-based emotion recognition has attracted increasing attention from the field of human-computer interaction (HCI). However, there are a number of challenges for machines to correctly recognize human emotional states. One problem is how to generalize the emotion model across time, since the brain may show different patterns of EEG for the same emotion experience at different time. This study investigated the feasibility of adding samples from different days to the training set to improve the generalization of the emotion classifier. Eight subjects participated in this experiment, and they were asked to watch different kinds of movie clips to produce neutral, positive or negative emotional states for different trials. Five sessions in five different days were conducted for each subject. EEG signals were recorded throughout the experiment. Support vector machine was used to perform a classification of the three emotional states, in which the training samples may come from 1, 2, 3 or 4 days' sessions but have a same number. The results showed that three categories were classified with average accuracies of 64.9%, 68.7%, 70.9%, and 73.0% respectively for 1-day, 2-day, 3-day and 4-day conditions. N-day condition represented the case in which data from N days were sent to train the SVM and the remaining 5-N days were used to form the testing set. Importantly, the accuracy had an increasing trend with the number of days in the training set for all subjects. Compared with 1-day condition, the accuracy of 4-day condition has about 10% improvement with a peak of 81.2% By analyzing the selected features in 4-day condition, we found the distributions of the selected features were relatively stable across days. The classifier did select emotion-relevant features and reject time-relevant features. These results suggested that incorporation of samples from different days to the training set could improve the generalization of an emotion classifier across time.
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Electroencefalografía , Emociones/clasificación , Máquina de Vectores de Soporte , Algoritmos , HumanosRESUMEN
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.