SET Domain Group 703 Regulates Planthopper Resistance by Suppressing the Expression of Defense-Related Genes.

Plant defense responses against insect pests are intricately regulated by highly complex regulatory networks. Post-translational modifications (PTMs) of histones modulate the expression of genes involved in various biological processes. However, the role of PTMs in conferring insect resistance remains unclear. Through the screening of a T-DNA insertion activation-tagged mutant collection in rice, we identified the mutant planthopper susceptible 1 (phs1), which exhibits heightened expression of SET domain group 703 (SDG703). This overexpression is associated with increased susceptibility to the small brown planthopper (SBPH), an economically significant insect pest affecting rice crops. SDG703 is constitutively expressed in multiple tissues and shows substantial upregulation in response to SBPH feeding. SDG703 demonstrates the activity of histone H3K9 methyltransferase. Transcriptomic analysis revealed the downregulation of genes involved in effector-triggered immunity (ETI) and pattern-triggered immunity (PTI) in plants overexpressing SDG703. Among the downregulated genes, the overexpression of SDG703 in plants resulted in a higher level of histone H3K9 methylation compared to control plants. Collectively, these findings indicate that SDG703 suppresses the expression of defense-related genes through the promotion of histone methylation, consequently leading to reduced resistance against SBPH. The defense-related genes regulated by histone methylation present valuable targets for developing effective pest management strategies in future studies. Furthermore, our study provides novel insight into the epigenetic regulation involved in plant-insect resistance.

Real-time regulation of room temperature based on individual thermal sensation using an online brain-computer interface.

Regulation of indoor temperature based on neurophysiological and psychological signals is one of the most promising technologies for intelligent buildings. In this study, we developed a system for closed-loop control of indoor temperature based on brain-computer interface (BCI) technology for the first time. Electroencephalogram (EEG) signals were collected from subjects for two room temperature categories (cool comfortable and hot uncomfortable) and used to build a thermal-sensation discrimination model (TSDM) with an ensemble learning method. Then, an online BCI system was developed based on the TSDM. In the online room temperature control experiment, when the TSDM detected that the subjects felt hot and uncomfortable, BCI would automatically turn on the air conditioner, and when the TSDM detected that the subjects felt cool and comfortable, BCI would automatically turn off the air conditioner. The results of online experiments in a hot environment showed that a BCI could significantly improve the thermal comfort of subjects (the subjective thermal comfort score decreased from 2.45 (hot uncomfortable) to 0.55 (cool comfortable), p < 0.001). A parallel experiment further showed that if the subjects wore thicker clothes during the experiment, the BCI would turn on the air conditioner for a longer time to ensure the thermal comfort of the subjects. This has further confirmed the effectiveness of TSDM model in evaluating thermal sensation under the dynamic change of room temperature and showed the model's good robustness. This study proposed a new paradigm of human-building interaction, which is expected to play a promising role in the development of human-centered intelligent buildings.

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments.

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features.

In the human-robot hybrid system, due to the error recognition of the pattern recognition system, the robot may perform erroneous motor execution, which may lead to falling-risk. While, the human can clearly detect the existence of errors, which is manifested in the central nervous activity characteristics. To date, the majority of studies on falling-risk detection have focused primarily on computer vision and physical signals. There are no reports of falling-risk detection methods based on neural activity. In this study, we propose a novel method to monitor multi erroneous motion events using electroencephalogram (EEG) features. There were 15 subjects who participated in this study, who kept standing with an upper limb supported posture and received an unpredictable postural perturbation. EEG signal analysis revealed a high negative peak with a maximum averaged amplitude of -14.75 ± 5.99 µV, occurring at 62 ms after postural perturbation. The xDAWN algorithm was used to reduce the high-dimension of EEG signal features. And, Bayesian linear discriminant analysis (BLDA) was used to train a classifier. The detection rate of the falling-risk onset is 98.67%. And the detection latency is 334ms, when we set detection rate beyond 90% as the standard of dangerous event onset. Further analysis showed that the falling-risk detection method based on postural perturbation evoked potential features has a good generalization ability. The model based on typical event data achieved 94.2% detection rate for unlearned atypical perturbation events. This study demonstrated the feasibility of using neural response to detect dangerous fall events.

Comparison of the COM-FCP inclination angle and other mediolateral stability indicators for turning.

BACKGROUND: Studies have shown that turning is associated with more instability than straight walking and instability increases with turning angles. However, the precise relationship of changes in stability with the curvature and step length of turning is not clear. The traditional center of mass (COM)-center of pressure (COP) inclination angle requires the use of force plates. A COM-foot contact point (FCP) inclination angle derived from kinematic data is proposed in this study as a measure of the stability of turning. METHODS: In order to generate different degrees of stability, we designed an experiment of walking with different curvatures and step lengths. Simultaneously, a novel method was proposed to calculate the COM-FCP inclination angles of different walking trajectories with different step lengths for 10 healthy subjects. The COM-FCP inclination angle, the COM acceleration, the step width and the COM-ankle inclination angles were statistically analyzed. RESULTS: The statistical results showed that the mediolateral (ML) COM-FCP inclination angles increased significantly as the curvature of the walking trajectories or the step length in circular walking increased. Changes in the ML COM acceleration, the step width and the ML COM-ankle inclination angle verified the feasibility and reliability of the proposed method. Additionally, the ML COM-FCP inclination angle was more sensitive to the ML stability than the ML COM-ankle inclination angle. CONCLUSIONS: The work suggests that it is more difficult to keep balance when walking in a circular trajectory with a larger curvature or in a larger step length. Essentially, turning with a larger angle in one step leads to a lower ML stability. A novel COM-FCP inclination angle was validated to indicate ML stability. This method can be applied to complicated walking tasks, where the force plate is not applicable, and it accounts for the variability of the base of support (BOS) compared to the COM-ankle inclination angle.

The effects of semantic congruency: a research of audiovisual P300-speller.

BACKGROUND: Over the past few decades, there have been many studies of aspects of brain-computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm. METHODS: The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms. RESULTS: The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm. CONCLUSIONS: In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.

A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study.

BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads. METHODS: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks. RESULTS: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution. CONCLUSIONS: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

[Research progress about different levels of cognitive recession using resting state functional connectivity network methods].

Normal brain aging and a serious of neurodegenerative diseases may lead to decline in memory, attention and executive ability and poorer quality of life. The mechanism of the decline is not clear now and is still a hot issue in the fields of neuroscience and medicine. A large number of researches showed that resting state functional brain networks based functional magnetic resonance imaging (fMRI) are sensitive and susceptive to the change of cognitive function. In this paper, the researches of brain functional connectivity based on resting fMRI in recent years were compared, and the results of subjects with different levels of cognitive decline including normal brain aging, mild cognitive impairment (MCI) and Alzheimer's disease (AD) were reviewed. And the changes of brain functional networks under three different levels of cognitive decline are introduced in this paper, which will provide the basis for the detection of normal brain aging and clinical diseases.

Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin.

There has been a long debate about the neural mechanism of event-related potentials (ERPs). Previously, no evidence or method was apparent to validate the two competing models, the evoked model and the oscillation model. One argument is whether the pre-stimulus brain oscillation could influence the following ERP. This study carried out an innovative visual oddball task experiment to investigate the dynamic process of visual evoked potentials. A period of stable oscillations of specified dominant frequencies and initial phases, i.e. the steady-state baseline, would be induced before responses to transient stimuli of different contrasts, which could overcome the artifact problem caused by the 'sorting' method. The result first revealed a 'three-period-transition' for the generation of visual evoked potentials by an objective decomposition. The ERP almost retained the preceding oscillation during the first period, provided an unstable negative potential in the second period, and generated the N1 component in the third period. The cross term analysis showed that the evoked model couldn't be the whole explanation for the ERP generation. Furthermore, the component analysis revealed that the N1 latency was sensitive to the initial phase under the low stimulus contrast (supporting the oscillation model) but not under the high stimulus contrast (supporting the evoked model). It demonstrated that the external stimulus contrast is a significant factor deciding the explicit model for ERPs. Our method and preliminary results may help reconcile the previous, seemly contradictory findings on the ERP mechanism.

EEG oscillatory patterns and classification of sequential compound limb motor imagery.

BACKGROUND: A number of studies have been done on movement imagination of motor sequences with a single limb. However, brain oscillatory patterns induced by movement imagination of motor sequences involving multiple limbs have not been reported in recent years. The goal of the present study was to verify the feasibility of application of motor sequences involving multiple limbs to brain-computer interface (BCI) systems based on motor imagery (MI). The changes of EEG patterns and the inter-influence between movements associated with the imagination of motor sequences were also investigated. METHODS: The experiment, where 12 healthy subjects participated, involved one motor sequence with a single limb and three kinds of motor sequences with two or three limbs. The activity involved mental simulation, imagining playing drums with two conditions (60 and 30 beats per minute for the first and second conditions, respectively). RESULTS: Movement imagination of different limbs in the sequence contributed to time-variant event-related desynchronization (ERD) patterns within both mu and beta rhythms, which was more obvious for the second condition compared with the first condition. The ERD values of left/right hand imagery with prior hand imagery were significantly larger than those with prior foot imagery, while the phase locking values (PLVs) between central electrodes and the mesial frontocentral electrode of non-initial movement were significantly larger than those of the initial movement during imagination of motor sequences for both conditions. Classification results showed that the power spectral density (PSD) based method outperformed the multi-class common spatial patterns (multi-CSP) based method: The highest accuracies were 82.86 % and 91.43 %, and the mean values were 65 % and 74.14 % for the first and second conditions, respectively. CONCLUSIONS: This work implies that motor sequences involving multiple limbs can be utilized to build a multimodal classification paradigm in MI-based BCI systems, and that prior movement imagination can result in the changes of neural activities in motor areas during subsequent movement imagination in the process of limb switching.

[Research on the Effects of 20Hz Frequency Somatosensory Vibration Stimulation on Electroencephalogram Features].

Somatosensory vibration can stimulate somatosensory area of human body,and this stimulation is tranferred to somatosensory nerves,and influences the somatic cortex,which is on post-central gyrus and paracentral lobule posterior of cerebral cortex,so that it alters the functional status of brain.The aim of the present study was to investigate the neural mechanism of brain state induced by somatosensory vibration.Twelve subjects were involved in the 20 Hz vibration stimulation test.Linear and nonlinear methods,such as relative change of relative power(RRP),Lempel-Ziv complexity(LZC)and brain network based on cross mutual information(CMI),were applied to discuss the change of brain under somatosensory vibration stimulation.The experimental results showed the frequency following response(FFR)by RRP of spontaneous electroencephalogram(EEG)in 20 Hz vibration,and no obvious change by LZC.The information transmission among various cortical areas enhanced under 20 Hz vibration stimulation.Therefore,20 Hz somatosensory vibration may be able to adjust the functional status of brain.

[Research Progress on the Interaction Effects and Its Neural Mechanisms between Physical Fatigue and Mental Fatigue].

Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.

[Brain Vigilance Analysis Based on the Measure of Complexity].

Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that: PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.

[Research on Mental Fatigue Detecting Method Based on Sleep Deprivation Models].

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.

[Non-linear research of alertness levels under sleep deprivation].

We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i. e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.

Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis.

BACKGROUND AND OBJECTIVE: Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance. METHODS: The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test. RESULTS: The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001). CONCLUSIONS: The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.

Using the Cocktail Party Effect to Add the Coding Dimension of Auditory Event Related Potential Brain-Computer Interface.

OBJECTIVE: The auditory event-related potential based brain-computer interface (aERP-BCI) is a classical paradigm of brain-computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect. METHODS: The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed. CONCLUSION: The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC. SIGNIFICANCE: The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.

Safety of Three-Dimensional Conformal Radiotherapy on the Sheep with Pulmonary Cystic Echinococcosis: A Preliminary Study.

PURPOSE: Radiotherapy showed the potential to effectively kill the cysts of pulmonary cystic echinococcosis (CE). However, little is known about its safety. This study was designed to investigate the safety of three-dimensional conformal radiotherapy (3D-CRT) on the normal lung tissue adjacent to the cyst and blood of sheep naturally infected with pulmonary CE. METHODS: Twenty pulmonary CE sheep were randomly divided into control group (n = 5) and radiation groups with a dose of 30 Gray (Gy) (n = 5), 45 Gy (n = 5), and 60 Gy (n = 5), respectively. Animals in control group received no radiation. Heat shock protein 70 (Hsp70), tumor growth factor-ß (TGF-ß), matrix metalloproteinase-2 (MMP-2) and MMP-9 in the lung tissues adjacent to the cysts, which were considered to be closely related to the pathogenesis of CE, were evaluated after 3D-CRT. A routine blood test was conducted. RESULTS: The results showed that there were multiple cysts of various sizes with protoscoleces in the lung tissues of sheep, and necrotic cysts were found after 3D-CRT. 3D-CRT significantly increased the mRNA level of Hsp70, enhanced the protein level of TGF-ß and slightly increased the expression of MMP-2 and MMP-9 in lung tissues adjacent to the cysts. 3D-CRT did not significantly alter the amount of WBC, HB and PLT in sheep blood. CONCLUSIONS: The results suggested that 3D-CRT may suppress the inflammation and induce less damage of the normal lung tissues and blood. We preliminarily showed that 3D-CRT under a safe dose may be used to treat pulmonary CE.

EEG feature comparison and classification of simple and compound limb motor imagery.

BACKGROUND: Motor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks. METHODS: Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). RESULTS: The induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%. CONCLUSIONS: The work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.

Improvements in Classification of Left and Right Foot Motor Intention Using Modulated Steady-State Somatosensory Evoked Potential Induced by Electrical Stimulation and Motor Imagery.

In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07± 13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.