A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel [Formula: see text]-shaped convolutional network ([Formula: see text]) aiming to address this issue. Unlike traditional network structures, [Formula: see text] incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full utilization of useful information. Moreover, considering that the fatigue state is a mental state involving temporal evolution, we proposed the novel long short-term memory (LSTM)-[Formula: see text]-shaped convolutional network (LSTM-[Formula: see text]), a parallel structure composed of LSTM and [Formula: see text] for fatigue detection, where [Formula: see text] extracts time-invariant features with location information, and LSTM extracts long temporal dependencies. We compared LSTM-[Formula: see text] with six competing methods based on two datasets. Results showed that the proposed algorithm achieved higher classification accuracy than the other methods, with 94.25% on EEG data (binary classification) and 82.19% on EOG data (triple classification). Additionally, the proposed algorithm exhibits low computational cost, good training stability, and robustness against insufficient training. Therefore, it is promising for further implementation of fatigue online detection systems.