Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.Comment: 8 pages, accepted by AAAI201

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Twisted Edwards-Form Elliptic Curve Cryptography for 8-bit AVR-based Sensor Nodes

Wireless Sensor Networks (WSNs) pose a number of unique security challenges that demand innovation in several areas including the design of cryptographic primitives and protocols. Despite recent progress, the efficient implementation of Elliptic Curve Cryptography (ECC) for WSNs is still a very active research topic and techniques to further reduce the time and energy cost of ECC are eagerly sought. This paper presents an optimized ECC implementation that we developed from scratch to comply with the severe resource constraints of 8-bit sensor nodes such as the MICAz and IRIS motes. Our ECC software uses Optimal Prime Fields (OPFs) as underlying algebraic structure and supports two different families of elliptic curves, namely Weierstraß-form and twisted Edwards-form curves. Due to the combination of efficient field arithmetic and fast group operations, we achieve an execution time of 5.8*10^6 clock cycles for a full 158-bit scalar multiplication on an 8-bit ATmega128 microcontroller, which is 2.78 times faster than the widely-used TinyECC library. Our implementation also shows that the energy cost of scalar multiplication on a MICAz (or IRIS) mote amounts to just 19 mJ when using a twisted Edwards curve over a 160-bit OPF. This result compares fairly well with the energy figures of two recently-presented hardware designs of ECC based on twisted Edwards curves

An Attitude Determination Method for Comprehensive Inspection Vehicle Based on Track Profile Registration

The attitude of the comprehensive inspection vehicle is one of the important factors that affect the accuracy of the inspection of metro line infrastructure, meanwhile the metro environment restricts the employment of common attitude determination methods. A new method of attitude determination is presented in this paper, which takes the track as reference and employs non-contact measurement to acquire the track profile simulta-neously. By registration of measurement track profile and the standard track profile, the relative position between the vehicle and the track reference can be calculated; and the instantaneous attitude of the vehicle can be determined by the matrix inverse calculation. The performance of the method is verified by an experiment using the road-rail comprehensive inspection vehicle

Who should provide a trade-in service under the online agency-selling mode?

In real world practice, trade-in programs are offered by either a manufacturer or an e-commerce platform. Parties that offer a trade-in service are faced with a trade-off between trade-in rebates and the residual income. By adopting the game theory, this paper explored the selection of trade-in provider with respect to a manufacturer and an e-commerce platform. The results show that in some cases, all trade-in models generated higher manufacturing costs than models with no trade-in program. However, in other cases, not all trade-in models can cope with manufacturing costs that are higher than those associated with models that have no trade-in program. Furthermore, both above two firms will offer the trade-ins when profits which they have obtained satisfied a certain condition. We also identified an interesting phenomenon whereby the manufacturer decided whether it wanted to delegate the trade-ins to the e-commerce platform or provide it jointly. The e-commerce platform can decide whether it wants to accept the delegation or jointly offer it. This study also obtain that trade-in models makes customers get more surplus and can produce greater environmental benefits. Moreover, both the customer surplus and the environmental benefits in delegated trade-in model is the same that in jointly trade-in model.</p

Anomaly Detection Based on Multiple Streams Clustering for Train Real-Time Ethernet

With the increasing traffic of train communication network (TCN), real-time Ethernet becomes the development trend. However, Train Control and Management System (TCMS) is inevitably faced with more security threats than before because of the openness of Ethernet communication protocol. It is necessary to introduce effective security mechanism into TCN. Therefore, we propose a train real-time Ethernet anomaly detection system (TREADS). TREADS introduces a multiple streams clustering algorithm to realize anomaly detection, which considers the correlation between the data dimensions and adopts the decay window to pay more attention to the recent data. In the experiment, the reliability of TREADS is tested based on the TRDP data set collected from the real network environment, and the models of anomaly detection algorithms are established for evaluation. Experimental results show that TREADS can provide a high reliability guarantee, besides, the algorithm can detect and analyze network anomalies more efficiently and accurately