Добірка наукової літератури з теми "WAVELET ENHANCED ICA"

Independent component analysis (ICA) has been proven to be a powerful tool for removing artifacts from electroencephalogram (EEG) recordings in the form of blind source separation (BSS). Independent components (ICs) come from undesired sources that are mixed with the useful signal, and the assessment of such ICs allows them to be detected. But the unwanted ICs also can contain some useful information. To overcome this problem, wavelet-enhanced ICA (wICA) can be used, and this method applies a wavelet threshold for each wavelet coefficient to suppress abnormal deformation in each wavelet coefficient. Using the wICA algorithm to suppress artifacts provides an EEG signal with less distortion in the amplitude and in the phase of the cerebral part of the EEG, and the cerebral part of the EEG can be estimated and obtained very similar to control conditions. However, the EEG signals are affected by various artifact components, and those that have the greatest influence are electromyography (EMG) and electrooculography (EOG). These artifacts may appear simultaneously, randomly or interruptedly, so a fixed threshold level is not really appropriate. We proposed a system including wICA integrated with an adaptive filter model, and this combination system can provide the best prediction of the impacts of artifacts to set up a threshold value that is adaptive and suitable. Our experimental results showed that are approach provided better rejection of artifacts than the wICA system.

Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.

Aiming at the problem of noise suppression in power lines, traditional noise suppression methods need to know prior knowledge and other defects. In this paper, blind source separation methods that do not need prior knowledge are selected. In the case of low signal-to-noise ratio, the basic independent component analysis algorithm has poor denoising effect. Therefore, this paper proposes a joint independent component analysis algorithm based on Wavelet denoising and Power independent component analysis (WD-PowerICA). In this work, firstly, the pseudo observation signal is constructed by weighted processing, and the blind separation model of single channel is transformed into a multi-channel determined model. Then, the proposed WD-PowerICA algorithm is used to separate noise and source signals. Finally, the simulation results demonstrate that the proposed algorithm in this paper can effectively separate noise and source signal under low SNR. At the same time, the stronger the α pulse noise is, the closer the WD-PowerICA separated signal is to the source signal. The proposed algorithm is better than the state of the art PowerICA algorithm.

Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise. Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise and measured noise are introduced into the independent component analysis (ICA) algorithm model, and then an enhanced Constrained Independent Component Analysis (CICA) method is proposed. The proposed method is implemented by combining the traditional Wavelet Transform (WT) with Constrained Independent Component Analysis (CICA). Results: In this method, the role of a supplementary step of WT before CICA analysis is explored to effectively reduce the influence of strong noise. Conclusion: Through the simulations and experiments, the results show that the proposed method can effectively decrease noise and enhance feature extraction effect of CICA method, and extract the desired gear fault feature, especially the low-frequency and weak fault feature.

Physiological signals, such as electroencephalogram (EEG), are used to observe a driver’s brain activities. A portable EEG system provides several advantages, including ease of operation, cost-effectiveness, portability, and few physical restrictions. However, it can be challenging to analyse EEG signals as they often contain various artefacts, including muscle activities, eye blinking, and unwanted noises. This study utilised an independent component analysis (ICA) approach to eliminate such unwanted signals from the unprocessed EEG data of 12 young, physically fit male participants between the ages of 19 and 24 who took part in a driving simulation. Furthermore, driver fatigue state detection was carried out using multichannel EEG signals obtained from O1, O2, Fp1, Fp2, P3, P4, F3, and F4. An enhanced modified z-score was utilised with features extracted from a time-frequency domain continuous wavelet transform (CWT) to elevate the reliability of driver fatigue classification. The proposed methodology offers several advantages. First, multichannel EEG analysis improves the accuracy of sleep stage detection, which is vital for accurate driver fatigue detection. Second, an enhanced modified z-score in feature extraction is more robust than conventional z-score techniques, making it more effective for removing outlier values and improving classification accuracy. Third, the proposed approach for detecting driver fatigue employs multiple machine learning classifiers, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANNs) that utilise Long Short-Term Memory (LSTM), and also machine learning techniques like Support Vector Machines (SVM). The evaluation of five classifiers was performed through 5-fold cross-validation. The outcomes indicate that the suggested framework attains exceptional precision in identifying driver fatigue, with an average accuracy rate of 96.07%. Among the classifiers, the ANN classifier achieved the most significant precision of 99.65%, and the SVM classifier ranked second with an accuracy of 97.89%. Based on the results of the receiver operating characteristic (ROC) and area under the curve (AUC) analysis, it was observed that all the classifiers had an outstanding performance, with an average AUC value of 0.95. This study’s contribution lies in presenting a comprehensive and effective framework that can accurately detect driver fatigue from EEG signals. ABSTRAK: Isyarat fisiologi, seperti elektroencefalogram (EEG), digunakan bagi memerhati aktiviti otak pemandu. Sistem EEG mudah alih menyediakan beberapa kelebihan, termasuk kemudahan operasi, keberkesanan kos, mudah alih dan sedikit sekatan fizikal. Namun, isyarat EEG mungkin sukar dianalisis kerana ia sering mengandungi pelbagai artifak, termasuk aktiviti otot, mata berkedip dan bunyi yang tidak diingini. Kajian ini menggunakan pendekatan analisis komponen bebas (ICA) bagi membuang isyarat tidak diperlukan daripada data EEG yang belum diproses daripada 12 peserta lelaki muda, cergas fizikal berumur 19 hingga 24 tahun yang mengambil bahagian dalam simulasi pemanduan. Tambahan, pengesanan keadaan lesu pemandu telah dijalankan menggunakan isyarat EEG berbilang saluran yang diperoleh dari O1, O2, Fp1, Fp2, P3, P4, F3, dan F4. Penambah baik skor z digunakan dengan ciri diekstrak daripada transformasi wavelet berterusan (CWT) domain frekuensi masa bagi meningkatkan kebolehpercayaan klasifikasi keletihan pemandu. Metodologi yang dicadangkan menawarkan beberapa kelebihan. Pertama, analisis EEG berbilang saluran meningkatkan ketepatan pengesanan peringkat tidur, penting bagi pengesanan keletihan pemandu secara tepat. Kedua, penambah baik skor z dalam pengekstrak ciri adalah lebih teguh daripada teknik skor z konvensional, menjadikannya lebih berkesan bagi membuang unsur luaran dan meningkatkan ketepatan pengelasan. Ketiga, pendekatan yang dicadangkan bagi mengesan keletihan pemandu menggunakan pelbagai pengelas pembelajaran mesin, seperti Rangkaian Neural Konvolusi (CNN), Rangkaian Neural Berulang (RNN), Rangkaian Neural Buatan (ANN) yang menggunakan Memori Jangka Pendek Panjang (LSTM), dan juga teknik pembelajaran mesin seperti Mesin Vektor Sokongan (SVM). Penilaian lima pengelas dilakukan melalui pengesahan silang 5 kali ganda. Dapatan kajian menunjukkan cadangan rangka kerja ini mencapai ketepatan yang luar biasa dalam mengenal pasti keletihan pemandu, dengan kadar ketepatan purata 96.07%. Antara kesemua pengelas, pengelas ANN mencapai ketepatan paling ketara sebanyak 99.65%, dan pengelas SVM menduduki tempat kedua dengan ketepatan 97.89%. Berdasarkan keputusan analisis ciri operasi penerima (ROC) dan kawasan di bawah lengkung (AUC), didapati semua pengelas mempunyai prestasi cemerlang, dengan purata nilai AUC 0.95. Sumbangan kajian ini adalah terletak pada rangka kerja yang komprehensif dan berkesan mengesan keletihan pemandu secara tepat melalui isyarat EEG.

We establish a powerful numerical algorithm to compute numerical solutions of coupled system of variable fractional order differential equations. Our numer?ical procedure is based on Bernstein polynomials. The mentioned polynomials are non-orthogonal and have the ability to produce good numerical results as compared to some other numerical method like wavelet. By variable fractional order differentiation and integration, some operational matrices are formed. On using the obtained matrices, the proposed coupled system is reduced to a system of algebraic equations. Using MATLAB, we solve the given equation for required results. Graphical presentations and maximum absolute errors are given to illustrate the results. Some useful features of our sachem are those that we need no discretization or collocation technique prior to develop operational matrices. Due to these features the computational complexity is much more reduced. Further, the efficacy of the procedure is enhanced by increasing the scale level. We also compare our results with that of Haar wavelet method to justify the useful?ness of our adopted method.

In the modern days, biometric identification is more promising and reliable to verify the human identity. Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binariezed Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary split is performed to divide the eight-bit binary of every pixel into four bit Least Significant Bits and four bit Most Significant Bits. DWT is applied on four bit MSB to extract the iris features. Then ICA is applied on approximate sub band to extract the significant details of iris. The obtained features are then applied on BSIF to obtain the enhanced response with final features. Finally features produced are matched with the test features using Euclidean distance classifier on CASIA database. The experiments are performed on proposed iris model using MATLAB 7.0 software considering various combinations of Person inside Database (PID’s) and Person outside Database (POD’s) to evaluate the recognition accuracy of the proposed iris model.

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for the classification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessing is included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement) as well as an eyeball movement artifact using the Discrete Wavelet Transformation (DWT). Denoising EEG signals from the AC power supply frequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accuracy of the processing stage (wavelet transform and neural network). The EEGs signals are categorized into normal and petit mal and clonic epilepsy by an expert neurologist. The categorization is confirmed by the Fast Fourier Transform (FFT) analysis. The dataset includes waves such as sharp, spike and spike‐slow wave. Through the Countinous Wavelet Transform (CWT) of EEG records, transient features are accurately captured and separated and used as classifier input. We introduce a two‐stage classifier based on the Learning Vector Quantization (LVQ) neural network localized in both time and frequency contexts. The particular coefficients of the Continuous Wavelet Transform (CWT) are networks. The simulation results are very promising and the accuracy of the proposed method obtained is of about 80%.

In this work we have presented a new approach towards wavelet enhanced ICA, we have used mMSE and kurtosis to detect the artifactual components automatically, Mahajan et al [28] displayed their performance in terms of sensitivity (90%) and specificity (98%), nMSE is good at recognizing EEG patterns because of its randomness and kurtosis is good at recognizing peaked signal because they have high kurtosis values. We compared our result with ICA based method zeroing ICA in terms of correlation, mutual information and coherence. Our result is far superior to it in all three terms, in correlation measure our method not only gives better results for unaffected recording channel but it improves the result from 0.44 to 0.58 for most affected recording channel, which means our method only suppresses the noise without introducing additional noise. When we compare the results in terms of mutual information it improves from 0.30 to 0.42 for most affected recording channel. When we study the coherence graph we notice that the wICA method is affecting those frequencies too which are not present in ocular artifacts frequency range but our method has only affected the frequency range 0-16 Hz which is ocular artifact’s frequency band. We can extend this work by using different mother wavelets to best approximate eye blink and other ocular artifacts.