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Adaptive Filtering with Fitted Noise Estimate (AFFiNE): Blink Artifact Correction in Simulated and Real P300 Data

by Kevin E. Alexander, Justin R. Estepp and Sherif M. Elbasiouny

Bioengineering 2024, 11(7), 707; https://doi.org/10.3390/bioengineering11070707 - 12 Jul 2024

Cited by 3 | Viewed by 2071

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter’s reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful. Full article

(This article belongs to the Collection Feature Papers in Advanced Computational Technologies for Biosignal Processing)

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19 pages, 18409 KB

Open AccessArticle

Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data

by Mary Judith Antony, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally and Rakesh Kumar Mahendran

Diagnostics 2023, 13(17), 2852; https://doi.org/10.3390/diagnostics13172852 - 3 Sep 2023

Cited by 11 | Viewed by 2601

Abstract

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity. Full article

(This article belongs to the Special Issue Biomedical Signal Processing and Analysis)

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17 pages, 6844 KB

Open AccessArticle

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

by Mary Judith Antony, Baghavathi Priya Sankaralingam, Rakesh Kumar Mahendran, Akber Abid Gardezi, Muhammad Shafiq, Jin-Ghoo Choi and Habib Hamam

Sensors 2022, 22(19), 7596; https://doi.org/10.3390/s22197596 - 7 Oct 2022

Cited by 51 | Viewed by 5890

Abstract

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. Full article

(This article belongs to the Special Issue Advances in IoMT for Healthcare Systems)

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