ABSTRACT
Introduction: In goal-directed tasks, visual prompts before the appearance of goals can make people ready in advance, which helps them to complete the movement better, and the presentation type of the visual prompt is very important. In previous studies, it has not been clear how different types of visual prompts guide attention in goal-directed tasks. Methods: According to the characteristics of goal-directed tasks, our research designed three different prompts: the cue prompt (featuring static arrow), the preparation prompt (involving dynamic countdown), and the combination prompt of cue and preparation information (simultaneously incorporating arrow and countdown). We used event-related potential components (CNV and P300) and graph theory indicators (clustering coefficient and characteristic path length) under the brain function connection to analyze the attention state of the brain. Results: The results showed that the combination prompts better guided the participants' sustained attention during the prompt stage, making them well prepared for the movement. Thus, after the target appeared, the participants had better executive control and achieved a faster response to the target. However, under the combination prompt, the participants consumed more attention resources during the prompt stage. Discussion: We believe that for the participants with impaired cognitive function, cue prompts or preparation prompts can be considered, which also play a role in guiding the participants' attention and helping them make motor preparations when less attention resources are consumed. This study provides a neurophysiological and behavioral foundation for the design of visual prompts in goal-directed tasks.
ABSTRACT
Introduction: In studies on consciousness detection for patients with disorders of consciousness, difference comparison of EEG responses based on active and passive task modes is difficult to sensitively detect patients' consciousness, while a single potential analysis of EEG responses cannot comprehensively and accurately determine patients' consciousness status. Therefore, in this paper, we designed a new consciousness detection paradigm based on a multi-stage cognitive task that could induce a series of event-related potentials and ERD/ERS phenomena reflecting different consciousness contents. A simple and direct task of paying attention to breathing was designed, and a comprehensive evaluation of consciousness level was conducted using multi-feature joint analysis. Methods: We recorded the EEG responses of 20 healthy subjects in three modes and reported the consciousness-related mean event-related potential amplitude, ERD/ERS phenomena, and the classification accuracy, sensitivity, and specificity of the EEG responses under different conditions. Results: The results showed that the EEG responses of the subjects under different conditions were significantly different in the time domain and time-frequency domain. Compared with the passive mode, the amplitudes of the event-related potentials in the breathing mode were further reduced, and the theta-ERS and alpha-ERD phenomena in the frontal region were further weakened. The breathing mode showed greater distinguishability from the active mode in machine learning-based classification. Discussion: By analyzing multiple features of EEG responses in different modes and stimuli, it is expected to achieve more sensitive and accurate consciousness detection. This study can provide a new idea for the design of consciousness detection methods.
ABSTRACT
In motor imagery (MI) brain-computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time-frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time-frequency-space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.
ABSTRACT
Myosin light chain 9 (MYL9) is a regulatory light chain of myosin, which plays an important role in various biological processes including cell contraction, proliferation and invasion. MYL9 expresses abnormally in several malignancies including lung cancer, breast cancer, prostate cancer, malignant melanoma and others, which is closely related to the poor prognosis, but the clinical significance for its expression varies with different types of cancer tissues. Further elucidating the molecular mechanism of MYL9 in various types of malignant tumor metastasis is of great significance for cancer prevention and treatment. At the same time, as a molecular marker and potential target, MYL9 may have great clinical value in the early diagnosis, prognosis prediction, and targeted treatment of malignant tumors.