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Non-brain contributions to electroencephalo-graphic (EEG) signals, often referred to as artifacts, can hamper the analysis of scalp EEG recordings. This is especially true when artifacts have large amplitudes (e.g., movement artifacts), or occur continuously (like eye-movement artifacts). Offline automated pipelines can detect and reduce artifact in EEG data, but no good solution exists for online processing of EEG data in near real time. Here, we propose the combined use of online artifact subspace reconstruction (ASR) to remove large amplitude transients, and online recursive independent component analysis (ORICA) combined with an independent component (IC) classifier to compute, classify, and remove artifact ICs. We demonstrate the efficacy of the proposed pipeline using 2 EEG recordings containing series of (1) movement and muscle artifacts, and (2) cued blinks and saccades. This pipeline is freely available in the Real-time EEG Source-mapping Toolbox (REST) for MATLAB (The Mathworks, Inc.).
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... One of the significant intelligent assistive systems depends on acquiring and transforming the activation of the Human Central Nervous System (CNS) processed data throughout a semi-automatic ICA (saICA) [16,18] full automated ICA (SASICA) [22] and only filtered data. ERD features in different frequency bands, alpha [8][9][10][11][12][13], beta [14][15][16][17][18][19][20][21][22][23][24][25], and alpha plus beta [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] were extracted and different channel configurations, C3 and C4, Region of Interest (RoI) around C3 and C4, and optimised algorithm methods for channel selection were compared as well as different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE). ...
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Thesis
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... Epochs were baseline-corrected to the pre-stimulus mean 38 . Muscle artifacts were rejected by the Automatic Artifact Rejection (AAR) 73 and independent component analysis (ICA) was used to remove eye movement artifacts 74 . Additionally, epochs containing high-amplitude artifacts or high-frequency muscle noise (visually inspected) were rejected from the analysis using a threshold-based method 75 . ...
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... Several studies that show sophisticated techniques for artifact removal are limited to offline systems [17], [18]. On the other hand, studies that eliminate artifacts in realtime use 32 or 64 channels [19], focusing on eliminating a single artifact [20], [21], or using a reference signal [22]. ...
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