Research on Brain Load Prediction Based on Machine Learning for High-Speed Railway Dispatching

EasyChair Preprint no. 10413

Abstract

In this paper, based on a simulation experiment platform, multimodal physiological data of the operator during emergency scenario processing are collected and processed. Specifically, for the ECG signal acquired by the ECG sensor, the noise is eliminated by using the method of stationary wavelet transform, and then the R-wave labeling is performed by the differential algorithm to obtain the HRV waveform and extract the time-domain, frequency-domain and nonlinear related features; for the multi-channel brainwave signal acquired by the EEG test system, the electrode positioning, potential re-referencing, filtering and noise removal are firstly performed using the eeglab toolkit For the eye-movement data collected by the eye tracker, the subject's fixation behavior was extracted using the position-distance threshold algorithm, and the fixation frequency and mean fixation time were calculated, together with the mean and standard deviation data of the pupils diameter, as the characteristics of the eye-movement dimension. In the process of regression prediction, a feature selection method based on entropy criterion was proposed in this paper. The results showed that the featureselected dataset achieved better performance in the regression prediction of the SVR model compared with the original feature set.

Keyphrases: Emergency Vehicle Management, Human Factors in Intelligent Transportation Systems, Incident Management

@Booklet{EasyChair:10413,
  author = {Dandan Bi and Xiaorong Meng and Wei Zheng},
  title = {Research on Brain Load Prediction Based on Machine Learning for High-Speed Railway Dispatching},
  howpublished = {EasyChair Preprint no. 10413},

  year = {EasyChair, 2023}}