Vandana Roy

Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal

The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal. Gaussian elimination is used for solving linear equation to calculate Eigen values which reduces the computation cost of the CCA method. This novel proposed method is tested against currently available artifact removal techniques using EEMD-CCA and wavelet transform. The performance is tested on synthetic and real EEG signal data. The proposed artifact removal technique is evaluated using efficiency matrices such as del signal to noise ratio (DSNR), lambda (λ), root mean square error (RMSE), elapsed time, and ROC parameters. The results indicate suitablity of the proposed algorithm for use as a supplement to algorithms currently in use.

A NLMS Based Approach for Artifacts Removal in Multichannel EEG Signals with ICA and Double Density Wavelet Transform

Presence of artifacts in electroencephalogram (EEG) signals is significant hurdles in analysis of spectral behavior. These artifacts are the low amplitude signals from unconscious ocular activity and muscles activity of human body. Since the source and noise in received signals originate from different sources, ICA method has been extensively revised for proper filtering. It involves the generating a set of individual components of given signal followed by rejection of unwanted artifacts. The results of this research show that considerable artifacts components persist in clean EEG signals. In this paper, we propose Double-Density DWT algorithm as the overhead computation with ICA for further filtering the signals. ICA segments the artifact peaks and DWT decompose them for suitable signal value. The Wavelet ICA suppression not only remove artifacts but also preserves the spectral (amplitude) and coherence (phase) characteristics of neural activity. In addition to this, NLMS filter is used at output of DWT to discard any trace of artifacts left in signal. The comparison of proposed scheme and conventional ICA indicates that NLMS filtered DWT-ICA outperforms the previous methods.

Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal

This chapter proposes an automatic method for artifact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on transform domain method having combination of data adaptive and non-data adaptive transform domain image denoising method to improve artifact elimination (ocular, high frequency muscle, and electrocardiogram (ECG) artifacts). The elimination of artifact from scalp EEGs is of substantial significance for both the automated and visual examination of underlying brainwave actions. These noise sources increase the difficulty in analyzing the EEG and obtaining clinical information related to pathology. Hence it is crucial to design a procedure to decrease such artifacts in EEG records. The role of a data adaptive transform domain, i.e., ICA to separate the signal from multichannel sources, then non-data adaptive transform, i.e., wavelet is applied to denoise the signal. The proposed methodology successfully rejected a good percentage of artifacts and noise, while preserving almost all the cerebral activity. The “denoised artifact-free” EEG presents a very good improvement compared with recorded raw EEG.

Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis

Electroencephalogram (EEG) signal is usually suffered from motion artifacts, generated randomly during signal acquisition timings. These artifacts sturdily affect the investigation and therefore, diagnosis of neural diseases from EEG signal. The artifact removal may cause loss of important information from the signal. Therefore, it is required to remove the motion artifacts and simultaneously preserve the desired information, which makes EEG artifact removal a vital task. Enhanced Empirical Mode Decomposition (EEMD) is the most widespread method used for artifact removal, as it is a data-driven based feature extraction method. In this research work the efficiency of various EEMD with different interpolation based artifact removal method have been compared. The EEMD is used to convert input single channel EEG signal to a multichannel signal, and in order to remove the randomness of motion artifact, CCA and DWT filtering were used successively. The performance of different interpolation based artifact removal methods have evaluated and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use as it offers improvements in DSNR and various other performance parameters.

Mth Order FIR Filtering for EEG Denoising Using Adaptive Recursive Least Squares Algorithm

In EEG Electroencephalogram signals Artifacts records are originated due to various factors as line interference, EOG (electro-Oculogram) and ECG (electrocardiogram). These noise sources upsurge the striving in analyzing the EEG and to procurement clinical information. Therefore, specific filters design is obligatory to diminution of such artifacts in EEG records. This research work anticipated an adaptive filtering method for eradicating ocular artifacts from EEG records by performing. Mth Order FIR Filtering on Adaptive RLS algorithm. In this paper, the method accuracy is estimated by utilizing virtual data quantitatively and equated with the precision of the time-domain regression method. The outcomes suggest that EEG channels are frequency dependent for transfer of ocular signal. The proposed adaptive filtering technique is more precise for denoising of EEG signals.