Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzkyxe2x80x93Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

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.