Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

Continuum robots present the great dexterity and compliance as dexterous manipulators to accomplish complex positioning tasks in confined anatomy during minimally invasive surgery. Tendon actuation is one of the most popular approaches, which is to insert the tendon to eccentrically go through and interact with the flexible backbone to accomplish compliant bends. However, hysteresis of tip trajectory of tendon actuated dexterous manipulators (TA-DMs) has been observed during the loading and unloading procedure, which is mainly caused by the hindered friction at discrete interactions between the actuation tendon and conduits. This paper aims to propose a general friction model to describe the interactions and friction profile at the multiple discrete contact points for tendon actuated dexterous manipulators under the history-dependent tendon tension. The friction model was integrated into the beam theory to describe the hysteresis and loading history-dependent behavior by solving the profiles of tendon force, normal force, and friction force, as well as the deflection of the dexterous manipulator. Experiments were carried out to validate the effectiveness of the proposed friction model. Results indicate that the friction model can successfully describe the discrete interaction and predict the deflection of dexterous manipulator subject to the different tendon loading histories. Furthermore, the effects of discrete friction to the tendon force propagation and the loading history-dependent behavior are discussed.

This work presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics' control of the cooperative adaptive cruise control (CACC) system. By incorporating a supervised network trained by real driving data into the actor-critic framework, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller.

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The authors would like to make the following corrections to their published paper in Energies [1]. [...]

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A mobile, easy to use, wireless dry contact EEG acquisition system is presented in this work. This system can potentially facilitate continuous in-home monitoring of electroencephalography (EEG) to diagnose ailments such as epilepsy. The system has also been validated with brain computer interface (BCI) paradigms that would enable physically disabled users to communicate.

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