Automated and Online Eye Blink Artifact Removal from Electroencephalogram Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEGu0027s interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal accuracy and ability of the approaches to retain neural information in an EEG signal. Analysis has discovered that the approaches have achieved more than 98% accuracy in detecting and removing eyeblink artifacts in real time. The approaches have produced very low reconstruction error as well, the least is 0.148 in average. These algorithms took about 12ms in average to clean a 1s length of EEG segment, which is fast enough to process the signals in real time.

A Multiclass EEG Signal Classification Model using Spatial Feature Extraction and XGBoost Algorithm Braincomputer interface is a framework which provides a communicating pathway between the human brain and neuroprosthetic devices. In this work, we have performed signal smoothing to fix unregulated electroencephalogram (EEG) fluctuations and to effectively collect hidden patterns in corresponding EEG spectra. To do it, we have applied a Savitzky Golay function because of its ability to preserve spectral properties without distorting it much. For feature selection, we have applied the Filter Bank Common Spatial Pattern (FBCSP) algorithm with the Principle Component Analysis (PCA). FBCSP creates 11352 features from EEG signals and PCA gradually reduces the features to 185 most significant features. These features are utilized in the classification process by the eXtreme Gradient Boosting (XGBoost) algorithm with suitable node split criteria to manage optimal tree height. A fivefold crossvalidation approach concludes the superior performance of XGBoost in terms of minimizing execution time (3.7 times faster in the training phase) and providing improved accuracy as compared to existing results. Our approach enhances classification accuracy (88.80%) by approximately 10% over regularized common spatial patterns (78.01% accuracy), 15% over shift variance approximation (73.84% accuracy) and 15% over Riemannian approach (74.77% accuracy). It also concludes that the preconsideration of the noise level in EEG spectra provides a better approximation.