Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

Abstract

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.