Enabling Safe ITS: EEG-Based Microsleep Detection in VANETs

Researchers nowadays are particularly focusing on the interpretation of EEG signals to understand and exploit the information they provide for brain activities. Deep learning architectures performing sleep staging have recently grown to their full potential with their ability to learn and interpret highly complex mathematical contexts. This has been catered to owing to the increasing availability of large EEG data sets. In this paper, we describe how sleep staging differs from microsleep prediction. We also provide a fresh methodology for the microsleep classification job that works with even less training data. Our proposed model exploits the attention-based mechanism that clubs the advantages available in Wavelet transform with Short Time Fourier Transform(STFT) Spectrogram. We also put forward a robust deep learning model that contains separate "timedependent" and "time-independent" parts, which can record contexts from the sequence of features and simultaneously learn intra-epoch relations. A single-electrode EEG signal was employed for our analysis to accommodate such procedures' social acceptance. For the task of microsleep detection on the MWT dataset, our model achieves fairly high accuracy rates (92% training and 89.9% testing accuracy), and an overall improvement in the kappa value by similar to 42%, as compared to prior novel approaches.