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Academic literature on the topic 'Ocular Artifacts (OA)'
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Journal articles on the topic "Ocular Artifacts (OA)"
1
Kumar, B. Krishna. "Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals." Biomedical and Pharmacology Journal 14, no. 1 (2021): 425–33. http://dx.doi.org/10.13005/bpj/2142.
Full textAbstract:
Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Principal Component Analysis is employed in denoising the EEG signals. This paper explains up to what level the scaling of principal components have to be done. This paper explains the number of levels of scaling the principal components to get the high quality EEG signal. The work has been carried out on different data sets and later estimated the SNR.
2
B.Krishna, Kumar. "Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 418–22. https://doi.org/10.35940/ijeat.F8956.129219.
Full textAbstract:
The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.
3
Gupta, Gauri Shanker, Dusmanta Kumar Mohanta, Subhojit Ghosh, Gunjan Bhavnesh Dave, Maanvi Bhatnagar, and Rakesh Kumar Sinha. "OCULAR ARTIFACTS ELIMINATION AND FEATURE EXTRACTION IN MOTOR IMAGERY-BASED BCI USING NONLINEAR ADAPTIVE FILTER." Biomedical Engineering: Applications, Basis and Communications 32, no. 02 (2020): 2050015. http://dx.doi.org/10.4015/s1016237220500155.
Full textAbstract:
The paper proposes a novel methodology of de-noising raw electroencephalogram (EEG) data from ocular artifacts (OAs) and alpha waves extraction from motor imagery-based signals that could be further utilized for brain–computer interface (BCI)-based applications. An algorithm based on discrete wavelet transform (DWT) and nonlinear adaptive filtering for the removal of OA is advocated, with an aim of making the process computationally intelligent. This algorithm has been tested on pre-recorded EEG dataset for BCI (Dataset IIIa; obtained from the website of the BCI Competition III). To further validate the competence of the proposed method, synthetic EEG signals were created, which were fused with white Gaussian noise. A total of 20 EEG signals were generated, half of which had added noise with a signal-to-noise ratio (SNR) of 10[Formula: see text]dB and other half had added noise of 5 dBSNR. Each signal contained 1000 samples with a sampling frequency of 250[Formula: see text]Hz. An optimum bandpass filter (FIR and IIR) for extraction of alpha waves has been suggested. FIR Equiripple filter is found most appropriate for the task as it has highest SNR and computes the response faster when compared with other filters. Among different mother wavelets, Daubechies 4 wavelet obtained using statistical thresholding denoises the EEG data most successfully. Correlation and root mean square error (RMSE) parameters show that the performance of nonlinear adaptive filter developed using nonlinear Volterra series has an edge over conventional adaptive filters for the intended purpose.
4
Gao, Chang, Lin Ma, and Haifeng Li. "An ICA/HHT Hybrid Approach for Automatic Ocular Artifact Correction." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1558001. http://dx.doi.org/10.1142/s021800141558001x.
Full textAbstract:
In the field of electroencephalogram (EEG) signal processing, the ocular artifact (OA), especially the blink is the most commonly observed, gives the largest disturbance, and can hardly be automatically corrected due to its sudden appearance and wide frequency band influence. In this paper, we focus on a 2-stage independent component analysis/Hilbert Huang transformation (ICA/HHT) hybrid OA correction method which realizes an automatic OA correction as well as a better information conservation to the OA distorted EEG data. In the 1st stage, the ICA is introduced to convert the raw EEG signals into a set of independent signal sources, i.e. a number of independent components (ICs). In the 2nd stage, the HHT is then applied to analyze the ICs in order to emphasize the differences between the OA related ICs and the normal EEG related ICs. On the intrinsic mode functions (IMFs) of each IC extracted by the empirical mode decomposition (EMD), the Hilbert spectrums can clearly indicate where an OA exists and make an automatic OA correction possible. EEG signal samples randomly picked from a variety of neuropsychology tasks were used to evaluate the proposed automatic OA correction method, and the outcomes approved an excellent OA correction performance as well as a better information conservation ability comparing to the well applied methods nowadays.
5
Bisht, Amandeep, Preeti Singh, Pardeep Kaur, and Geeta Dalal. "Identification of ocular artifact in EEG signals using VMD and Hurst exponent." Journal of Basic and Clinical Physiology and Pharmacology, October 24, 2024. http://dx.doi.org/10.1515/jbcpp-2024-0027.
Full textAbstract:
Abstract Objectives Electroencephalographic (EEG) readings are usually infected with unavoidable artifacts, especially physiological ones. One such physiological artifact is the ocular artifacts (OAs) that are generally related to eyes and are characterized by high magnitude and a specific spike pattern in the prefrontal region of the brain. During the long-duration EEG acquisition, the retrieval of important information becomes quite complicated in prefrontal regions as ocular artifacts dominate the EEG recorded, making it difficult to discern underlying brain activity. Methods With the progress and development in signal processing techniques, artifact handling has become a progressive field of investigation. This paper presents a framework for the detection and correction of ocular artifacts. This study emphasizes improving the quality and reducing the time complexity by using higher-order statistics (HOS) for artifact identification and variational mode decomposition (VMD) for OA correction. Results An overall SNR of 14 dB, MAE of 0.09, and PSNR of 33.59 dB has been attained by the proposed framework. Conclusions It was observed that the proposed HOS-VMD surpassed the state-of-the-art mode decomposition techniques.
6
V, Krishnaveni, Jayaraman S, and Ramadoss K. "Application of Mutual Information based Least dependent Component Analysis (MILCA) for Removal of Ocular Artifacts from Electroencephalogram." February 23, 2007. https://doi.org/10.5281/zenodo.1079500.
Full textAbstract:
The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
7
"Denoising of EEG Signals using Wavelets and SIMULINK Techniques." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 335–39. http://dx.doi.org/10.35940/ijrte.c5113.018520.
Full textAbstract:
The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task due to the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using both matlab coding as well as SIMULINK DWT and IDWT blocks by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks is explained in detail in the paper under the methodology heading. In this paper the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it.
8
"Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models." International Journal of Engineering and Advanced Technology 9, no. 2 (2019): 418–22. http://dx.doi.org/10.35940/ijeat.f8956.129219.
Full textAbstract:
The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.