Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering

Objective.the P300-based brain-computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. While various approaches have been explored to enhance the SNR of P300 signals, the impact of VEPs has been largely overlooked. The main objective of this study is to investigate how VEPs impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.Approach.our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.Main results.analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs demonstrates improved performance in P300-based BCIs. This improvement is verified through several experiments conducted with real P300 data.Significance.this study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.

The simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand

Background: Although changes in performance during the learning of various sports skills have been studied, however, how these changes at the brain level is still unknown. The aim of this study was to investigate simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand. Methodology: The samples consisted of 14 non-athlete students with an average age of 23 ± 2.5, which were divided into two group dominant hand (7) and non-dominant hand (7). Repeated measures ANOVA were used to measure data at the execution level and changes in EEG activity. Results: The results of this study at the performance level showed a significant reduction in the absolute error of dart throwing and at the same time at the brain level increased EEG activity in frontal and parietal-posterior regions along with decreased central area activity in acquisition and retention stages in both groups (P<.05). Also, there was a significant difference between the activity of EEG pattern in the dominant and non-dominant hand groups except for two channels AF3 and PO4 (P<.05). Conclusion: In general, the results of this study showed that along with relatively constant changes in performance during dart skill learning, relatively constant changes in EEG activity pattern occur, so that the concept of motor learning is also visible at the brain level. Also, the results of this study supported the existence of the different motor program for dominant and non-dominant hand control in the conditions of bilateral transfer control.

ERP Detection Based on Smoothness Priors

OBJECTIVE

Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed.

METHODS

The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under the Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in post-stimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection.

RESULTS

The presented method performs better than the competing algorithms and improves the classification accuracy.

CONCLUSION

SGLRT can be employed as a powerful means for different ERP detection schemes.

SIGNIFICANCE

The proposed scheme is opening a new direction in ERP identification which provides better classification results compared to several popular ERP detection methods.