Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG

Abstract

The growing demand for semi-autonomous human–machine systems has led to an increased requirement for human fatigue detection. Direct and invasive approaches for microsleep detection include cognitive computing methods using Brain-Computer Interfaces (BCI). The contextual integration of multi-channel or heterogeneous signals for sleep staging remains a formidable challenge. In addition, the cost of acquiring many signals is significantly higher than that of acquiring a single signal. Consequently, researchers have recently attempted to utilize single-channel EEG over multi-channel acquisition systems for sleep staging. The Fast Fourier Transform (FFT) has been widely used in previous research findings for spectral analysis of complex time-series data streams. In contrast to the FFT, we utilize here, for the first time, the Welch Transform which can give higher stability in noise reduction for spectral analysis. Specifically, we provide a novel method to implement the short-time Welch transform (STWT) as an enhanced technique for the spectro-temporal analysis of single-electrode EEG signals. Further, our proposed model utilizes attention-based spatial and channel-wise inter-dependencies using a one-dimensional causal convolutional neural network (CNN) to extract contextual features automatically. Finally, we demonstrate an end-to-end proof of concept for our data extraction, adaptive data resampling, manual feature extraction, and deep-neural network-based modeling architecture. Comparative simulation results using the benchmark, maintenance of wake-fullness test (MWT) dataset for microsleep detection during automobile transportation, show that our proposed end-to-end system, utilizing novel STWT-based enhanced spectro-temporal analysis, outperforms current state-of-the-art methods, delivering 95% and 89% test accuracy for the case of temporal and spectral data inputs, respectively.