Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain–computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. Approach. The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. Main results. The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. Significance. From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.

Drivers' cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed indepth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, realtime processing, and between-subject classification accuracy.

This paper investigates the lateral stability and maneuverability in long combination vehicles (LCVs), namely semi-trucks with 28-ft doubles, 28-ft triples, and 33ft doubles, using TruckSim. In recent years, due to the rapid increase of E-commerce cargo transport demands, trucks with multiple trailers have been used with increasing frequency on U.S. highways. The most common configuration is 28-ft doubles, although in some states, 28-ft triples and longer doubles, such as 33-ft trailers, are also allowed. LCVs provide operational advantages in terms of loading and unloading, ease of distribution, and other key logistics that are superior to the conventional 53-ft trailers that are commonly used for bulk cargo over long hauls. The vast proliferation of LCVs on U.S. highways heightens awareness of their dynamics, including lateral stability and maneuverability which are strongly tied to highway safety. This study provides a comparative evaluation of the lateral characteristics for tractors with 28-ft double, 28-ft triple, and 33-ft double trailers. In particular, the likelihood of rollovers, rearward amplification, and off-tracking are analyzed among those LCVs using the multi-domain dynamic models developed in

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