The recognition of driving action based on EEG signals using wavelet-CSP algorithm


The ability of recognizing driving actions could help building a more advanced driving assitance system, and could even be applied in automated driving to improve the driving safety. In this paper, we investigate the offline recognition of three classes of driving actions (turning left, turning right and braking), based on electroencephalography (EEG) signals. A simulated experiment was conducted to collect EEG data of participants. The proposed algorithm includes Wavelet Analysis and Common Spatial Patterns (CSP), to extract the discriminative features. The classification results were obtained using the Linear Discriminant Analysis (LDA). The results yielded an average single trial classification accuracy of 70.25% for all subjects, showing the discrimination of different actions and the correlation between driving actions and EEG signals.

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)
Gan Huang
Gan Huang

My research interests include Neural Modulation, Brain Computer Interface and Neural Prosthetics.