57 research outputs found

    Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

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    Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs

    Bayesian Robust Tensor Factorization for Incomplete Multiway Data

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    We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-tt distribution that associates an individual hyperparameter with each element independently. For model learning, we develop an efficient closed-form variational inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world datasets demonstrate the superiorities of our method from several perspectives.Comment: in IEEE Transactions on Neural Networks and Learning Systems, 201

    An adaptive weighted self-representation method for incomplete multi-view clustering

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    For multi-view data in reality, part of its elements may be missing because of human or machine error. Incomplete multi-view clustering (IMC) clusters the incomplete multi-view data according to the characters of various views of the instances. Recently, IMC has attracted much attention and many related methods have been proposed. However, the existing approaches still need to be developed and innovated in the following aspects: (1) Current methods only consider the differences of different views, while the different influences of instances, as well as distinguishes between missing values and completed values are ignored. (2) The updating scheme for weighting matrix in adaptive weighted algorithms usually relies on an optimization sub-problem, whose optimal solution may not be easy to achieve. (3) The adaptive weighted subspace algorithms that can recover the incomplete data are anchor types. The randomness of the anchor matrix may cause unreliability. To tackle these limitations, we propose an adaptive weighted self-representation (AWSR) subspace method for IMC. The AWSR method tunes the weighting matrix adaptively in accordance with the views of different instances and the recovery process of the missing values. The low rank and smoothness constraints on the representation matrix make the subspace reveal the underlying features of the dataset accurately. We also analyze the convergence property of the block coordinate method for our optimization model theoretically. Numerical performance on five real-world data shows that the AWSR method is effective and delivers superior results when compared to other eight widely-used approaches considering the clustering accuracy (ACC), normalized mutual information (NMI) and Purity
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