The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain–computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.

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Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end‐to‐end delay analysis in wireless networks that employs inter‐session network coding. Prior work on performance analysis in wireless network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the delay of each flow in the network. The theoretical basis of our approach is network calculus. In order to use network calculus to analyze the performance of traffic flows in the network, we have to address three specific problems: identifying traffic flows, characterizing broadcast links, and measuring coding opportunities. We propose solutions for these problems and discuss the practical issues when applying the approach in practice. We make three main contributions. First, we obtain theoretical formulations for computing the queueing delay bounds of traffic flows in wireless networks with network coding. Second, with the formulations, we figure out the factors that affect the queueing delay of a flow and find that first‐in first‐out scheduling cannot fully exploit the benefit of network coding. Third, in order to exploit our findings, we introduce a new scheduling scheme that can improve the performance of current practical wireless network coding. Copyright © 2012 John Wiley & Sons, Ltd.

In order to achieve reliable event detection, wireless sensor networks (WSN) should guarantee the reliable transport of sensor data from sensor nodes to the sink. So it is very important to keep a limited deviation between sensor data (the data sensed by sensor node) and sink data (the data delivered to applications). We put forward a data reliability notion to depict this limited deviation in this paper. Noticing that sensor data vary slightly in relative short time unless certain events occur, we construct a small subset of sensor data as key data, and provide reliable transport only for these key data instead of for all sensor data. This solution decreases retransmission and energy while keeping data reliability. Based on the data reliability notion, we propose DREET, a data-reliable energy-efficient transport layer protocol. Simulation results show that DREET provides an optimal event detection rate and relative standard deviation (RSD) between sensor data and sink data, by introducing a tiny increase to energy consumption (no more than 1.97% in our simulation)