This work investigates the effects of visual evoked potentials on P300-based brain-computer interfaces and proposes a regularized version of the xDAWN algorithm to improve these systems.

P300 speller is a brain-machine interface that lets us type by focusing on our desired letter. When the desired letter intensifies, there is a P300 in electroencephalography signals. P300 has a low signal-to-noise ratio; thus, it is hard to be detected. This work presents a new approach toward P300 detection based on a three-step spatial filter (known as principal ERP reduction technique), as the preprocessing stage, in conjunction with linear discriminant analysis, as the classifier. This method is applied to real data, and the results show the effectiveness of the proposed method, especially in the case of small training sets.

Signal activity detection is an important problem in many fields of study, including communication, radar, and biomedical signal processing. As we know, many signals have a smooth template compared to noise. In this work, this extra info on the signal structure is used to improve conventional signal detection methods. The suggested method is applied to a P300 dataset to detect this template in background electroencephalography. The results demonstrate the effectiveness of the method in low signal-to-noise ratios.

Power-line interference is an important intruder source while recording weak potentials like biomedical signals. Hence, designing a high-quality notch filter with desirable gain and phase attributes is of interest. In this work, we present a unit-gain zero-phase notch filter based on the sinusoid dynamic of the PLI. This filter is achieved by simultaneous minimization of the observation and dynamic model noises. Furthermore, the bandwidth of this filter could easily alter based on different signal-to-noise ratio situations. The assessments of the filter for PLI cancelation from synthetic and real data demonstrate its power and effectiveness.

Event-related potentials (ERPs) are weak activities in electroencephalography (EEG) in response to different stimuli. Detecting these potentials is of great value for many diagnostic and practical purposes. However, this is rather hard because these signals have a low signal-to-noise ratio. In this work, we present a novel approach toward ERP detection based on the fact that this template has a smooth structure compared to background EEG. A generalized likelihood ratio test is adopted to realize our algorithm. The proposed method is investigated for ERP detection from real datasets, and it demonstrates to be very effective compared to conventional state-of-the-art methods.