Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain-computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients' data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients' data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it works for other types of data.

The electroencephalogram (EEG) signals tend to have poor time-frequency localization when analysis techniques involve a fixed set of basis functions such as in short-time Fourier transform (STFT) and wavelet transform (WT). These signals also exhibit highly non-stationary characteristics and suffer from low signal-to-noise ratio (SNR). As a result, there is often poor task detection accuracy and high error rates in designed braincomputer interfacing (BCI) systems. In this paper, a novel preprocessing method is proposed to automatically reconstruct the EEG signal by selecting the intrinsic mode functions (IMFs) based on a median frequency measure. Multivariate empirical mode decomposition (MEMD) is used to decompose the EEG signals into a set of IMFs. The reconstructed EEG signal has high SNR and contains only information correlated to a specific motor imagery task. The common spatial pattern (CSP) is used to extract features from the reconstructed EEG signals. The linear discriminant analysis (LDA) and support vector machine (SVM) have been utilized in order to classify the features into left hand motor imagery (LHMI) and right hand motor imagery (RHMI) tasks. Our experimental results on the BCI competition IV dataset 2A show that the proposed method with fifteen channels outperforms bandpass filtering with twenty-two channels (> 1%) and by > 9 % (p = 0:0078) with raw EEG signals, > 13% (p = 0:0039) with empirical mode decomposition (EMD) based filtering and > 17 % (p = 0:0039) with discrete wavelet transform (DWT) based filtering.

With the booming of social media, sentiment analysis has developed rapidly in recent years. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose the traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new prospective for modern ITSs. Methods and models in TSA are proposed in this paper, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection. Two cases were studied to demonstrate the efficiency of our method: the “yellow light rule” and “fuel price” in China. Our work will help the development of TSA and its applications.