The existing 3D target detection network based on feature layer fusion of multi-view images and lidar point cloud fusion is mostly fused by directly splicing the multi-sensor features output by the backbone or the BEV features under the unified perspective of the two modalities. The features obtained by this method will be affected by the original data feature modality conversion and multi-sensor feature fusion`s effect. Aiming at this problem, a 3D object detection network based on feature fusion based on channel attention is proposed to improve the feature aggregation ability of BEV feature fusion, thereby improving the representation ability of the fused features. The experimental results on the nuScenes open source dataset show that compared with the baseline network, the overall feature grasp of the object is increased, and the average orientation error and average speed error are reduced by 4.9% and 4.0%, respectively. In the process of automatic driving, It can improve the vehicle's ability to perceive moving obstacles on the road, which has certain practical value.
Objective: The posterior parietal cortex (PPC) is involved in cognitive attentional activity, according to neuroimaging research. Its specific role in the brain network responsible for attention need to be further identified. Approach: In this study, an attention network test (ANT) was designed. 1-Hz low-frequency repeated transcranial magnetic stimulation (rTMS) was performed over the right PPC. Behavioral and EEG data were analyzed before and after stimulation. Main results: The behavioral results showed that the frontoparietal network was directly connected to spatial orientation and executive functions. Low-frequency TMS produced an inhibitory effect in opposition to the learning effect, which persisted 24 hours after stimulation and was most pronounced under spatial cues. Event-related potentials (ERP) results showed that the stimulation inhibited the posterior parietal brain neural activity, and in particular, had a significant inhibitory effect on spatial attention. Significance: PPC is crucial in attentional orienting and executive functions. Exploring ways to intervene in brain activity has significant implications for people to recover attentional function.
Brain-computer interface (BCI) is a technology that enables direct communication with machines through brain signals. As BCI technology evolves into new applications, the need for robust feature extraction technology will only continue to increase. In BCI tasks with small amplitude variations, such as low-contrast oddball classification, classification and recognition of EEG signals are challenging. Inspired by fine-grained classification in the field of image classification, this study innovatively uses and integrates some fine-grained classification strategies based on convolutional neural networks to improve the classification performance of the system through feature learning and feature fusion at part-level and multi-scale. Ten subjects were recruited to perform the subthreshold low-contrast Oddball task. The results showed that Fine-grained EEG CNN had a better performance in small-difference EEG signal classification compared with the classical EEG convolution neural network. Therefore, we provide a valuable new strategy for improving the classification performance of small-difference EEG signals.
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