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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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... On the other hand, EEG signals can be contaminated by biological and non-biological artifactual activity, which might lead to a decrease in the signal-to-noise ratio (SNR), hampering the accurate and reliable detection of brain activity and reducing the system's overall performance [7,14,15]. Therefore, several preprocessing techniques have been proposed to enhance the SNR of EEG signals, including both online [16][17][18] and offline [4,7,16,19] approaches. After mitigating EEG artifacts, the next vital step in BCI systems is identifying and extracting relevant signal features (Fig. 1). ...
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... The default parameters in ROAST are used for the rest of the calculations. To determine the stimulus intensity for each brain region, the MNI coordinates of the brain regions are used to calculate the affine transformation matrix using the REST software package [31]. This matrix is then used to obtain the corresponding voxel coordinates (x, y, z) for each brain region. ...
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