Sparse Common Spatial Pattern Research Articles

Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform

Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method for automated seizure identification from the EEG signal is proposed utilizing the sparse common spatial pattern (sCSP) and the adaptive short-time Fourier transform-based synchrosqueezing transform (adaptive FSST). The sCSP is utilized to select optimal channels and discriminate seizure states. In order to enhance the time–frequency representation of multi-component EEG signals and reduce noise and interferences, the selected channels are imported into adaptive FSST. The frequency spectrum of the adaptive FSST is separated into distinct segments concentrated on a particular frequency to improve seizure detection by adapting the EEG signal decomposition to the rhythmic component of seizures. Thus, a simple linear classifier is all that is needed to identify seizures thanks to this collection of algorithms since the extracted features from each segment’s instantaneous amplitude and frequency and logvariance of each selected channel could be highly discriminative. Using linear classifiers for these discriminative features avoids overfitting and training complexity inherent in most non-linear classifiers and deep learning approaches. The proposed approach is compared to previous high-performance algorithms using the CHB-MIT epileptic EEG database. This method outperformed most of the best findings in the literature regarding mean performance, with mean sensitivity, specificity, and accuracy of 98.44%, 99.19%, and 98.81%, respectively. Thus, the proposed method’s outstanding performance demonstrates that it could significantly decrease EEG interpretation load on specialists and be a powerful tool for an epilepsy diagnosis.

Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis

BackgroundThe classification of psychological tasks such as motor imagery based on electroencephalography (EEG) signals is an essential issue in the brain computer interface (BCI) system. The feature extraction is an important issue for improving classification accuracy of BCI system. New MethodFor extracting discriminative features, common spatial pattern (CSP) is an effective feature extraction method. However, features extracted by CSP are dense, and even feature patterns are repeatedly selected in the feature space. A sparse CSP algorithm is proposed, which embeds the sparse techniques and iterative search into the CSP. To improve the classification performance, two regularization parameters are added to the traditional linear discriminant analysis (LDA). ResultsThe sparse CSP algorithm can select several channels of EEG signals with the most obvious features. The improved regularized discriminant analysis is used to solve the singularity problem and improve the feature classification accuracy.Comparison with Existing Method(s): The proposed algorithm was evaluated by the data set I of the IVth BCI competition and our dataset. The experimental results of the BCI competition dataset show that accuracy of the improved algorithm is 10.75 % higher than that of the traditional algorithm. Comparing with the currently existing methods for the same data, it also shows excellent classification performance. The effectiveness of the improved algorithm is also shown in experiments on our dataset. ConclusionsIt sufficiently proves that the improved algorithm proposed in this paper improves the classification performance of motor intent recognition.

Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces.

Common spatial pattern (CSP) is one of the most successful feature extraction algorithms for brain-computer interfaces (BCIs). It aims to find spatial filters that maximize the projected variance ratio between the covariance matrices of the multichannel electroencephalography (EEG) signals corresponding to two mental tasks, which can be formulated as a generalized eigenvalue problem (GEP). However, it is challenging in principle to impose additional regularization onto the CSP to obtain structural solutions (e.g., sparse CSP) due to the intrinsic nonconvexity and invariance property of GEPs. This article reformulates the CSP as a constrained minimization problem and establishes the equivalence of the reformulated and the original CSPs. An efficient algorithm is proposed to solve this optimization problem by alternately performing singular value decomposition (SVD) and least squares. Under this new formulation, various regularization techniques for linear regression can then be easily implemented to regularize the CSPs for different learning paradigms, such as the sparse CSP, the transfer CSP, and the multisubject CSP. Evaluations on three BCI competition datasets show that the regularized CSP algorithms outperform other baselines, especially for the high-dimensional small training set. The extensive results validate the efficiency and effectiveness of the proposed CSP formulation in different learning contexts.

Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.

Greedy solutions for the construction of sparse spatial and spatio-spectral filters in brain computer interface applications

In the original formulation of common spatial pattern (CSP), all recording channels are combined when extracting the variance as input features for a brain computer interface (BCI). This results in overfitting and robustness problems of the constructed system. Here, we introduce a sparse CSP method in which only a subset of all available channels is linearly combined when extracting the features, resulting in improved generalization in classification. We propose a greedy search based generalized eigenvalue decomposition approach for identifying multiple sparse eigenvectors to compute the spatial projections. We evaluate the performance of the proposed sparse CSP method in binary classification problems using electrocorticogram (ECoG) and electroencephalogram (EEG) datasets of brain computer interface competition 2005. We show that the results obtained by sparse CSP outperform those obtained by traditional (non-sparse) CSP. When averaged over five subjects in the EEG dataset, the classification error is 12.3% with average sparseness level of 11.6 compared to 18.4% error obtained by the traditional CSP with 118 channels. The classification error is 10% with sparseness level of 7 compared to that of 13% obtained by the traditional CSP using 64 channels in the ECoG dataset. Furthermore, we explored the effectiveness of the proposed sparse methods for extracting sparse common spatio-spectral patterns (CSSP).

Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization

Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated ℓ1 penalty. Unlike the RQ, this new objective function depends on the magnitude of the sparse filter. The SSP method is employed to classify the multiclass ECoG and two class EEG data sets. We compared our results with a recently introduced sparse CSP solution based on ℓ0 norm. Our method outperforms the standard CSP method and provides comparable results to ℓ0 norm based solution and it is associated with less computational complexity. We also conducted several simulation studies on the effect of noisy channel and intersession variability on the performance of the CSP and sparse filters.

Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI

Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4, and Cz).