Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 397 - 397

Published: Jan. 10, 2025

Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting decisions misaligned with intentions. To address this limitation, study introduces a pioneering human-centric spatial cognition detecting system based on electroencephalogram (EEG) signals. Unlike conventional EEG-based that focus intention recognition or hazard perception, the proposed can further extract across two dimensions: relative distance and orientation. It consists of components: EEG signal preprocessing decoding, enabling to make more contextually aligned regarding targets drivers on. enhance detection accuracy cognition, we designed novel decoding method called Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained fine-grained temporal features signals different scales incorporates Squeeze-and-Excitation module evaluate importance electrodes. The DTFNet outperforms existing methods, achieving 65.67% 50.65% three-class tasks 84.46% 70.50% binary tasks. Furthermore, investigated dynamics observed perception occurs slightly later than their orientation, providing valuable insights into aspects processing.

Language: Английский

Decoding Natural Images from EEG Signals Using a learnable Multi-band Spatio-Temporal Encoder

Published: Jan. 1, 2025

Language: Английский