Adaptive SubjectSpecific Bayesian Spectral Filtering for Single Trial Eeg Classification Despite recent advances in signal and information processing, human brain remains the most intriguing signal processing unit with inconceivable abilities to analyze and fuse various multimodal, streaming signals adaptively in real time. With recent advancements in sensors and computational technologies, brain computer interfacing (BCI) via electroencephalography (EEG) signals have received extensive attention for establishing an alternative form of communication with our brain. In this paper, we propose a subjectspecific filtering framework, referred to as the regularized doubleband Bayesian (RB2B) spectral filtering, couples three main feature extraction categories, namely filterbank solutions, regularized techniques, and optimized Bayesian mechanisms to enhance the classification accuracy by simultaneously taking advantage of the three processing techniques. Furthermore, data collection experiments1 are performed to investigate different effects of stimulus on the performance of the proposed RB2B. In this regard, four different protocols are designed and implemented by introducing visual and voice stimuli. Finally, the paper investigates effects of adaptive trimming of EEG epochs resulting in an adaptive and subjectspecific solution. Experimental results show that the proposed RB2B filter noticeably outperforms its counterparts.

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.