Titlebook: Human Brain and Artificial Intelligence; Third International Xiaomin Ying Conference proceedings 2023 The Editor(s) (if applicable) and Th

花費(fèi)

DSNet: EEG-Based Spatial Convolutional Neural Network for Detecting Major Depressive Disordertion performance with the accuracy of 91.69% via the leave-one-subject-out (LOSO) cross-validation strategy compared to other DL models. The experimental results demonstrate that DSNet can effectively extract information on spatial differences between depressed and normal individuals and could be a

撕裂皮肉

SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based that the weights of Fp1, Fp2, O1 and O2 electrodes were slightly greater. It demonstrated that the prefrontal lobe and occipital lobe may be possibly important brain regions for MDD and BD recognition. Overall, this study shows the effectiveness of the proposed model in EEG-based automatic diagnosis

搬運(yùn)工

SUGAR

Salient Object Detection with Fusion of RGB Image and Eye Tracking Datadata and the RGB image features. (3) The comparative experiments on the two datasets show that the performance of the proposed method exceeds that of the mainstream algorithms and can achieve effective SOD.

畢業(yè)典禮

JIBE

Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causalityleft MA, left MA and left SA, and left SA and right SA for both finger motor execution and motor imagination, and the most important connection in motor function was from premotor area to primary motor area.

VERT

A Hybrid Brain-Computer Interface for?Smart Car Controled that the hybrid BCI system achieved an average accuracy of 97.65%, an average information translate rate (ITR) of 43.50 bit/min, and an average false positive rate (FPR) of 0.70 event/min, thus demonstrating the effectiveness of our proposed system.

neutrophils

A Spiking Neural Network for Brain-Computer Interface of Four Classes Motor Imagery experiment on the publicly released dataset achieves the accuracy that is comparable to the previous work of one-Dimension convolution neural network (1D-CNN). Meanwhile, the number of parameters of proposed network is about 1/10 of that in 1D-CNN. This study reveals the great potential of the SNN

先驅(qū)

Brain Controlled Manipulator System Based on Improved Target Detection and Augmented Reality Technolcts participating in the grasping experiment, according to the experimental results, the grasping accuracy of the brain-controlled manipulator system is 92%, which verifies the effectiveness and portability of the system.

背帶

Optimization of Stimulus Color for SSVEP-Based Brain-Computer Interfaces in Mixed Reality the red one on the blue and black backgrounds, while the red stimulus outperformed the white one on the green and white backgrounds. The color contrast ratio (CCR) between the background and stimulus colors correlated positively with SSVEP recognition accuracy. In mixed reality, SSVEP-based BCIs mu