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

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

Recommendations on the Internet of Things: Requirements, Challenges, and Directions

© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation

Trop2 expression contributes to tumor pathogenesis by activating the ERK MAPK pathway

<p>Abstract</p> <p>Background</p> <p>Trop2 is a cell-surface glycoprotein overexpressed by a variety of epithelial carcinomas with reported low to restricted expression in normal tissues. Expression of Trop2 has been associated with increased tumor aggressiveness, metastasis and decreased patient survival, but the signaling mechanisms mediated by Trop2 are still unknown. Here, we studied the effects murine Trop2 (mTrop2) exerted on tumor cellular functions and some of the signaling mechanisms activated by this oncogene.</p> <p>Results</p> <p>mTrop2 expression significantly increased tumor cell proliferation at low serum concentration, migration, foci formation and anchorage-independent growth. These <it>in vitro </it>characteristics translated to increased tumor growth in both subcutaneous and orthotopic pancreatic cancer murine models and also led to increased liver metastasis. mTrop2 expression also increased the levels of phosphorylated ERK1/2 mediating cell cycle progression by increasing the levels of cyclin D1 and cyclin E as well as downregulating p27. The activation of ERK was also observed in human pancreatic ductal epithelial cells and colorectal adenocarcinoma cells overexpressing human Trop2.</p> <p>Conclusions</p> <p>These findings demonstrate some of the pathogenic effects mediated by mTrop2 expression on cancer cells and the importance of targeting this cell surface glycoprotein. This study also provides the first indication of a molecular signaling pathway activated by Trop2 which has important implications for cancer cell growth and survival.</p

Room-temperature antiferromagnetic CrSe monolayer with tunable metal-insulator transition in ferroelectric heterostructures

Recently, there has been a rapidly growing interest in two-dimensional (2D) transition metal chalcogenide monolayers (MLs) due to their unique magnetic and electronic properties. By using an evolutionary algorithm and first-principles calculations, we report the discovery of a previously unexplored, chemically, energetically, and thermodynamically stable 2D antiferromagnetic (AFM) CrSe ML with a N\'eel temperature higher than room temperature. Remarkably, we predict an electric field-controllable metal-insulator transition (MIT) in a van der Waals (vdW) heterostructure comprised of CrSe ML and ferroelectric Sc2CO2. This tunable transition in CrSe/Sc2CO2 heterostructure is attributed to the change in the band alignment between CrSe and Sc2CO2 caused by the ferroelectric polarization reversal in Sc2CO2. Our findings suggest that 2D AFM CrSe ML has important potential applications in AFM spintronics, particularly in the gate voltage conducting channel.Comment: 13 Pages, 4 Figure