Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

Finding an optimal dictionary of different wavelet types using sparse modeling to denoise ECG signal

Sparse signal modeling often reconstructs a signal with few atoms from a pre-defined dictionary. Hence the choice of wavelet dictionary that represents the sparsity of the target signal is crucial in sparse modeling approach. The challenge of finding an optimal dictionary of different wavelet types using sparse denoising model (SDM) to denoise ECG signal is investigated in this work. A method of finding an optimal wavelet dictionary from a set of orthogonal wavelet sub-dictionaries by the means of the best correlation with ECG signal, is developed. The highly correlated sub-dictionaries from three wavelet dictionaries, namely daubechies, symlets, coiflets and discrete cosine transform are combined to construct an overcomplete dictionary. The weight of Akaike’s information criterion and the signal-to-noise ratio improvement are considered as a criterion to evaluate the performance of the proposed SDM. The results indicate that multi-wavelet dictionary of different types is highly sparse and efficient in denoising the target signal, e.g., ECG.

A New Combinational Technique in Image Steganography

Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data.

Impact of optimal scaling coefficients in bi-orthogonal wavelet filters on compressed sensing

Purpose In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model. Design/methodology/approach A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model. Findings Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively. Originality/value This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.