Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis

Judith, A. Mary; Priya, S. Baghavathi; Mahendran, Rakesh Kumar

doi:10.1016/j.bspc.2021.103452

Cited by 19 publications

(6 citation statements)

References 25 publications

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“…After preprocessing, the signals are put through an online whitening procedure to make the mixed sources independent. According to the authors in [19], the ORICA algorithm correctly and successfully separates the artifact‐contaminated MI EEG recorded source. It is possible to minimize the computing complexity by processing the loop in blocks rather than via each sample.…”

Section: Methodsmentioning

confidence: 99%

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

A¹,

S²,

Mahendran

et al. 2022

Asian Journal of Control

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Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4‐class MI‐based BCIs. In this study, 4‐class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two‐phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair‐wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non‐stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two‐stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.

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Section: Methodsmentioning

confidence: 99%

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

A¹,

S²,

Mahendran

et al. 2022

Asian Journal of Control

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show abstract

“…To make the mixed sources independent, the signals after pre-processing are subjected to the online whitening process. The artifact-contaminated MI EEG recording source is accurately and successfully separated using the ORICA algorithm [ 26 ]. For each iteration, the whitening matrix X and the de-mixing matrix W are computed according to Equations (1) and (3), respectively, by adding the block-update rule on matrix W .…”

Section: A-svm With Orica-csp Frameworkmentioning

confidence: 99%

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Antony¹,

Sankaralingam²,

Mahendran

et al. 2022

Sensors

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An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain—computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes’ motor images, namely Dataset 2a of BCI Competition IV.

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“…There are various methods to remove artifacts from photoacoustic images in current studies. Traditional methods such as singular value decomposition [20] and Gaussian filters [21] can only remove some artifacts caused by strong acoustic reflection [22]. Photoacoustically guided focused ultrasound (PAFUSion) technology uses an innovative method to eliminate reflection artifacts by simulating the photoacoustic(PA) field, but it needs to meet the requirements of ultrasound and photoacoustic image matching, which may reduce the frame rate and bring errors in the case of tissue movement [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

He,

Chen,

Jiang

et al. 2024

Applied Sciences

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Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.

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Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis

Cited by 19 publications

References 25 publications

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

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