The Spatio-Temporal Equalization for Evoked or Event-Related Potential Detection in Multichannel EEG Data

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.

EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots' SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots' cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces

Objective. Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.Approach. The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.Main results. The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the steady-state visual evoked potential amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.Significance. The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.

Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI

Objective.Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data.Approach.The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the 'non-control state detection' methods, this algorithm was based on the 'statistical inspection-rejection decision' mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates.Main results.Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of97.2±2.6%and the average information transfer rate (ITR) of106.3±32.0 bitsmin-1. At the same time, the average false alarm rate in the 240 s resting state test was0.607±0.602 min-1. In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min^-1in two free spelling experiments.Significance.This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.

Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response

Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.