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Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal-noise ratio and incomplete EEG signals), and difficulties in capturing the inconspicuous relationships between EEG signals and certain brain activities. Most existing works either only consider EEG as chain-like sequences neglecting complex dependencies between adjacent signals or requiring pre-processing such as transforming EEG waves into images. In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements and instructions effectively learning the compositional spatio-temporal representations of raw EEG streams. Extensive experiments on a large scale movement intention EEG dataset (108 subjects,3,145,160 EEG records) have demonstrated that both models achieve high accuracy near 98.3% and outperform a set of baseline methods and most recent deep learning based EEG recognition models, yielding a significant accuracy increase of 18% in the cross-subject validation scenario. The developed models are further evaluated with a real-world BCI and achieve a recognition accuracy of 93% over five instruction intentions. This suggests the proposed models are able to generalize over different kinds of intentions and BCI systems.

Recognition of human intention based on Electroencephalography (EEG) signals attracts strong research interest in pattern recognition because of its promising applications that enable non-muscular communications and controls. Over the past few years, most EEG-based recognition works make significant efforts to learn extracted features to explore specific patterns between a segment of EEG signals and the corresponding activities. Unfortunately, vectorization-based feature representations, either vector-like or matrix-like ones, suffer from massive signal noise and difficulties of exploiting signal correlations between adjacent sensors of EEG signals. Most importantly, EEG signals are represented by one unique frequency and then fed into the subsequent learning model. Neglecting different frequencies of EEG signals can be detrimental to activity recognition because a particular frequency of EEG signals is more helpful to recognize some activities. Inspired by this idea, we propose to extract EEG signals with different frequencies and introduce a novel Multi-task deep learning model to learn the human intentions. We have conducted extensive experiments on a publicly available EEG benchmark dataset and compared our method with many state-of-the-art algorithms. The experimental results demonstrate that the proposed Multi-task deep recurrent neural network outperforms all the compared methods in a multi-class scenario.

Abstract-Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations.Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-ofthe-art methods.

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