Security in Transformer Visual Trackers: A Case Study on the Adversarial Robustness of Two Models.

Visual object tracking is an important technology in camera-based sensor networks, which has a wide range of practicability in auto-drive systems. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It causes such transformer-based applications to be exposed to security threats. In this work, the security of the transformer model was investigated with an important component of autonomous driving, i.e., visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, and thus, adversarial attacks were implemented as the security threats to conduct the investigation. First, adversarial examples were generated on top of video sequences to degrade the tracking performance, and the frame-by-frame temporal motion was taken into consideration when generating perturbations over the depicted tracking results. Then, the influence of perturbations on performance was sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrated that the executed adversarial examples were effective on the performance drops of the transformer-based visual tracking. White-box attacks showed the highest effectiveness, where the attack success rates exceeded 90% against transformer-based trackers.

[A review on electroencephalogram based channel selection].

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles.

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle's historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.

[Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials].

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.

[Research advances in non-invasive brain-computer interface control strategies].

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

A novel predictive model based on inflammatory markers to assess the prognosis of patients with HBV-related acute-on-chronic liver failure: a retrospective cohort study.

BACKGROUND: Systemic inflammatory response is closely related to the development and prognosis of liver failure. This study aimed to establish a new model combing the inflammatory markers including neutrophil/lymphocyte ratio (NLR) and red blood cell distribution width (RDW) with several hematological testing indicators to assess the prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). METHODS: A derivation cohort with 421 patients and a validation cohort with 156 patients were recruited from three hospitals. Retrospectively collecting their clinical data and laboratory testing indicators. Medcalc-15.10 software was employed for data analyses. RESULTS: Multivariate analysis indicated that RDW, NLR, INR, TBIL and Cr were risk factors for 90-day mortality in patients with HBV-ACLF. The risk assessment model is COXRNTIC = 0.053 × RDW + 0.027 × NLR + 0.003 × TBIL+ 0.317 × INR + 0.003 × Cr (RNTIC) with a cut-off value of 3.08 (sensitivity: 77.89%, specificity: 86.04%). The area under the receiver operating characteristics curve (AUC) of the RNTIC was 0.873 [95% CI(0.837-0.903)], better than the predictive value of MELD score [0.732, 95% CI(0.687-0.774)], MELD-Na [0.714, 95% CI(0.668-0.757)], CTP[0.703, 95% CI(0.657-0.747)]. In the validation cohort, RNTIC also performed a better prediction value than MELD score, MELD-Na and CTP with the AUC of [0.845, 95% CI(0.778-0.898)], [0.768, 95% CI (0.694-0.832)], [0.759, 95% CI(0.684-0.824)] and [0.718, 95% CI(0.641-0.787)] respectively. CONCLUSIONS: The inflammatory markers RDW and NLR could be used as independent predictors of 90-day mortality in patients with HBV-ACLF. Compared with MELD score, MELD-Na and CTP, RNTIC had a more powerful predictive value for prognosis of patients with HBV-ACLF.

Characteristics of within-household varicella transmission events associated with school outbreaks in Shanghai, China, 2009-2018.

Transmission of varicella occurs frequently in schools and households. We investigated the characteristics of varicella cases derived from within-household transmission and the modes of varicella transmission between school and household settings in Shanghai, China, from 2009 to 2018. Within-household transmission occurred in 278 households, of which 134 transmission events were between children. Sixty-one household varicella transmission events may be attributed to isolation procedures for infected students during school outbreaks, and 7.6% of school outbreaks were caused by schoolchildren cases derived from within-household transmission. The frequency of 'school-household-school' transmission adds an additional layer of complexity to the control of school varicella outbreaks. Administration of varicella vaccine as post-exposure prophylaxis after exposure is considered to be an effective measure to control varicella spread within households and schools.

Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks.

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs.

Living Supramolecular Polymerization of an Aza-BODIPY Dye Controlled by a Hydrogen-Bond-Accepting Triazole Unit Introduced by Click Chemistry.

An aza-BODIPY dye 1 bearing two hydrophobic fan-shaped tridodecyloxybenzamide pendants through 1,2,3-triazole linkages was synthesized by a click reaction and characterized. 1 H NMR studies indicated that dye 1 exhibited variable conformations through intramolecular H-bonding interaction, which is beneficial for the polymorphism of aggregation. The thermodynamic, structural, and kinetic aspect of the supramolecular polymerization of dye 1 was investigated by UV/Vis absorption spectroscopy, IR spectroscopy, AFM, TEM, and SEM. Biphasic aggregation pathways of dye 1, leads to the formation of off-pathway, metastable Agg. I and thermodynamically stable Agg. II with distinct H-aggregation spectra and nanoscale morphology. The living manner of the supramolecular polymerization of dye 1 was demonstrated in seeded polymerization experiments with temperature-modulated successive cooling-heating cycles.

Norovirus shedding among symptomatic and asymptomatic employees in outbreak settings in Shanghai, China.

BACKGROUND: Norovirus (NoV) is recognized as a leading cause of acute gastroenteritis (AGE) outbreaks in settings globally. Studies have shown that employees played an important role in the transmission mode during some NoV outbreaks. This study aimed to investigate the prevalence of NoV infection and duration of NoV shedding among employees during NoV outbreaks, as well as factors affecting shedding duration. METHODS: Specimens and epidemiological data were collected from employees who were suspected of being involved in the transmission or with AGE symptoms during NoV outbreaks in Xuhui District, Shanghai, from 2015 to 2017. Specimens were detected using real-time RT-PCR to determine whether or not employees had become infected with NoV. Specimens were collected every 3-7 days from NoV-infected employees until specimens became negative for NoV. RESULTS: A total of 421 employees were sampled from 49 NoV outbreaks, and nearly 90% of them (377/421) were asymptomatic. Symptomatic employees showed significantly higher prevalence of NoV infection than asymptomatic ones (70.5% vs. 17.0%, P < 0.01). The average duration of NoV shedding was 6.9 days (95% confidence interval: 6.1-7.7 days) among 88 NoV-infected individuals, and was significantly longer in symptomatic individuals than in asymptomatic ones (9.8 days vs. 5.6 days, P < 0.01). In Cox proportional-hazards model, after adjusting age and gender, symptoms was the only factor associated with duration of NoV shedding. CONCLUSIONS: Compared with asymptomatic employees, symptomatic employees had higher prevalence of NoV infection and longer durations of NoV shedding. Since NoV shedding duration among NoV-infected employees tends to be longer than their isolation time during outbreaks, reinforcement of hygiene practices among these employees is especially necessary to reduce the risk of virus secondary transmissions after their return to work.

Coupled Cooperative Supramolecular Polymerization: A New Model Applied to the Competing Aggregation Pathways of an Amphiphilic aza-BODIPY Dye into Spherical and Rod-Like Aggregates.

Based on our studies on biphasic self-assembly behavior of an amphiphilic BF2 -azadipyrromethene (aza-BODIPY) dye 1, a new analytical model to quantitatively describe the thermodynamic properties of the aggregation involving two competing supramolecular polymerization processes is proposed. In this model, the formation of the metastable as well as the thermodynamically stable aggregates was considered to follow a nucleated polymerization mechanism. The numerical calculation based on the new model gives insight into the formation of different species in such complicate aggregate systems. Moreover, the aggregation of the biphasic self-assembly processes for dye 1 was investigated by concentration-dependent UV/Vis spectroscopy. The experimental data were analyzed by using the new model to evaluate the thermodynamic parameters including aggregation constants, the size of nuclei, and the cooperativity the two types of aggregates.

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.

[Intraocular Pressure Sensor Based on a Contact Lens].

Intraocular pressure detection has a great significance for understanding the status of eye health, prevention and treatment of diseases such as glaucoma. Traditional intraocular pressure detection needs to be held in the hospital. It is not only time-consuming to doctors and patients, but also difficult to achieve 24 hour-continuous detection. Microminiaturization of the intraocular pressure sensor and wearing it as a contact lens, which is convenient, comfortable and noninvasive, can solve this problem because the soft contact lens with an embedded micro fabricated strain gauge allows the measurement of changes in corneal curvature to correlate to variations of intraocular pressure. We fabricated a strain gauge using micro-electron mechanical systems, and integrated with the contact lens made of polydimethylsiloxane (PDMS) using injection molding. The experimental results showed that the sensitivity was 100. 7 µV/µm. When attached to the corneal surface, the average sensitivity of sensor response of intraocular pressure can be 125.8 µV/mm Hg under the ideal condition.

Inhibition study of alanine aminotransferase enzyme using sequential online capillary electrophoresis analysis.

We report the study of several inhibitors on alanine aminotransferase (ALT) enzyme using sequential online capillary electrophoresis (CE) assay. Using metal ions (Na(+) and Mg(2+)) as example inhibitors, we show that evolution of the ALT inhibition reaction can be achieved by automatically and simultaneously monitoring the substrate consumption and product formation as a function of reaction time. The inhibition mechanism and kinetic constants of ALT inhibition with succinic acid and two traditional Chinese medicines were derived from the sequential online CE assay. Our study could provide valuable information about the inhibition reactions of ALT enzyme.

CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-Target RSVP-BCI Tasks.

OBJECTIVE: The Rapid Serial Visual Presentation (RSVP) paradigm facilitates target identification in a rapid picture stream, which is applied extensively in military target surveillance and police monitoring. Most researchers concentrate on the single target RSVP-BCI whereas the study of dual-target is scarcely conducted, limiting RSVP application considerably. METHODS: This paper proposed a novel classification model named Common Representation Extraction-Targeted Stacked Convolutional Autoencoder (CRE-TSCAE) to detect two targets with one nontarget in RSVP tasks. CRE generated a common representation for each target class to reduce variability from different trials of the same class and distinguish the difference between two targets better. TSCAE aimed to control uncertainty in the training process while requiring less target training data. The model learned a compact and discriminative feature through the training from several learning tasks so as to distinguish each class effectively. RESULTS: It was validated on the World Robot Contest 2021 and 2022 ERP datasets. Experimental results showed that CRE-TSCAE outperformed the state-of-the-art RSVP decoding algorithms and the Average ACC was 71.25%, improving 6.5% at least over the rest. CONCLUSION: It demonstrated that CRE-TSCAE showed a strong ability to extract discriminative latent features in detecting the differences among two targets with nontarget, which guaranteed increased classification accuracy. SIGNIFICANCE: CRE-TSCAE provided an innovative and effective classification model for dual-target RSVP-BCI tasks and some insights into the neurophysiological distinction between different targets.

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain-Computer Interface.

The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

DTTZ suppresses ferroptosis and reverses mitochondrial dysfunction in normal tissues affected by chemotherapy.

Conventional antineoplastic therapies cause severe normal tissue damage and existing cytoprotectants with acute toxicities or potential tumor protection limit their clinical application. We evaluated the selective cytoprotection of 2,2-dimethylthiazolidine hydrochloride in this study, which could protect normal tissue toxicity without interfering antineoplastic therapies. By using diverse cell lines and A549 xenograft model, we discovered a synthetic aminothiol 2,2-dimethylthiazolidine hydrochloride selectively diminished normal cellular ferroptosis via SystemXc-/Glutathione Peroxidase 4 pathway upon antineoplastic therapies without interfering the anticancer efficacy. We revealed the malignant and non-malignant tissues presenting different energy metabolism patterns. And cisplatin induces disparate replicative stress, contributing to the distinguishable cytoprotection of 2,2-dimethylthiazolidine in normal and tumor cells. The compound pre-application could mitigate cisplatin-induced normal cellular mitochondrial oxidative phosphorylation (OXPHOS) dysfunction. Pharmacologic ablation of mitochondria reversed 2,2-dimethylthiazolidine chemoprotection against cisplatin in the normal cell line. Combined, these results provide a potential therapeutic adjuvant to selectively diminish normal tissue damages retaining antineoplastic efficacy.

Semantic image sorting method for RSVP presentation.

Objective.The rapid serial visual presentation (RSVP) paradigm, which is based on the electroencephalogram (EEG) technology, is an effective approach for object detection. It aims to detect the event-related potentials (ERP) components evoked by target images for rapid identification. However, the object detection performance within this paradigm is affected by the visual disparity between adjacent images in a sequence. Currently, there is no objective metric to quantify this visual difference. Consequently, a reliable image sorting method is required to ensure the generation of a smooth sequence for effective presentation.Approach. In this paper, we propose a novel semantic image sorting method for sorting RSVP sequences, which aims at generating sequences that are perceptually smoother in terms of the human visual experience.Main results. We conducted a comparative analysis between our method and two existing methods for generating RSVP sequences using both qualitative and quantitative assessments. A qualitative evaluation revealed that the sequences generated by our method were smoother in subjective vision and were more effective in evoking stronger ERP components than those generated by the other two methods. Quantitatively, our method generated semantically smoother sequences than the other two methods. Furthermore, we employed four advanced approaches to classify single-trial EEG signals evoked by each of the three methods. The classification results of the EEG signals evoked by our method were superior to those of the other two methods.Significance. In summary, the results indicate that the proposed method can significantly enhance the object detection performance in RSVP-based sequences.