MetaBCI: An open-source platform for brain-computer interfaces.

BACKGROUND: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. METHOD: This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. RESULTS: The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. CONCLUSIONS: MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Aerobic Exercise Changes Low-Frequency Functional and Effective Connectivity in Cognitive Load Task.

As is well known, cognitive performances are highly influenced by cognitive load, so it is meaningful to find some ways to effectively reduce the cognitive load. In particular, aerobic exercise is a promising way. However, the neural evidence is still lacking in understanding how aerobic exercise minimizes cognitive load. To solve the problem, this study adopted the N-back task in both the before (BE) and after (AE) aerobic exercise periods, behavioral and EEG data were recorded from 21 participants. Functional connectivity was constructed by the weighted phase lag index (WPLI), and effective connectivity was constructed by the partially directed coherent (PDC). Consequently, by comparing the connection strength and pattern of BE and AE, it is found that in low-frequency (0~8 Hz), aerobic exercise could enhance the connection strength of WPLI networks under high cognitive load, and increase the importance of the forehead region in the communication of PDC networks under low cognitive load. These results could advance our understanding of the underlying mechanisms of how aerobic exercise modulates cognitive load.

SKDCPM algorithm can improve the single-trial decoding performance of very similar error-related potentials.

Error related potential (ErrP) is an effective control signal for the brain-computer interface (BCI). Current ErrP decoding methods can only distinguish right and wrong mental states. However, in real scenarios, error conditions often contain more detailed information, such as the degree of error, which would induce very similar ErrPs. Distinguishing such ErrPs effectively is of vital importance to provide more detailed information for optimizing BCIs. Hereto, a major challenge is the EEG differences of very similar ErrPs are very small. Thus, it is necessary to develop new efficient method for decoding very similar ErrPs. This study newly proposed an algorithm named shrinkage discriminant canonical pattern matching (SKDCPM), and compared its decoding results with the linear discriminant analysis (LDA), shrinkage LDA (SKLDA), stepwise LDA (SWLDA), Bayesian LDA (BLDA) and the DCPM, which were algorithms commonly used for ErrP decoding. A data set of 18 subjects was built, it had four conditions, i.e., right (0°), errors with varying degrees, i.e., 45°, 90°, 180° deviation from the predicted direction. As a result, the SKDCPM had high balanced accuracy (BACC) in right-wrong classification (0° vs. others). More importantly, it achieved a grand averaged BACC of 69.54% with the highest up to 74.25%, which outperformed all the other algorithms in very similar ErrPs decoding (45° vs. 90° vs. 180°) significantly. This study could provide new decoding methods for developing the ErrP-based BCI system.

Temporal prediction changes motor-related EEG phase synchronization and network centrality in alpha and beta band.

Much neurophysiological evidence revealed motor system is involved in temporal prediction. However, It remains unknown how temporal prediction influences motor-related neural representations. Thus, more neural evidence is needed to understand better how temporal prediction influences the motor. This study designed a rhythmic finger-tap task and formed three temporal prediction conditions, i.e., 1000ms temporal prediction, 1500ms temporal prediction, and no temporal prediction. Behavioral and EEG data from 24 healthy subjects were recorded. The weighted phase lag index was calculated to measure the degree of phase synchronization. Eigenvector centrality and betweenness centrality were used to measure brain connectivity. Behavioral results showed that tap-visual asynchronies were decreased when temporal prediction existed. Phase synchronization results showed, compared to no temporal prediction, the alpha-band phase synchronization between the frontal and central area was reduced in 1000ms temporal prediction, and the beta-band phase synchronization between the frontal and parietal area was decreased in 1500ms temporal prediction. As to the brain connectivity, compared to no temporal prediction condition, the eigenvector centrality of the left frontal in 1500ms temporal prediction was decreased in the alpha band, and the betweenness centrality of the right temporal in 1000ms temporal prediction was reduced in the alpha-band. These results can provide new neural evidence for a better understanding of temporal prediction and motor interactions.

[Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

A SSVEP-Based Brain-Computer Interface With Low-Pixel Density of Stimuli.

The brain-computer interface (BCI) based on the steady-state visual evoked potential (SSVEP) has drawn widespread attention due to its high communication speed and low individual variability. However, there is still a need to enhance the comfort of SSVEP-BCI, especially considering the assurance of its effectiveness. This study aims to achieve a perfect balance between comfort and effectiveness by reducing the pixel density of SSVEP stimuli. Three experiments were conducted to determine the most suitable presentation form (flickering square vs. flickering checkerboard), pixel distribution pattern (random vs. uniform), and pixel density value (100%, 90%, 80%, 70%, 60%, 40%, 20%). Subjects' electroencephalogram (EEG) and fatigue scores were recorded, while comfort and effectiveness were measured by fatigue score and classification accuracy, respectively. The results showed that the flickering square with random pixel distribution achieved a lower fatigue score and higher accuracy. EEG responses induced by stimuli with a square-random presentation mode were then compared across various pixel densities. In both offline and online tests, the fatigue score decreased as the pixel density decreased. Strikingly, when the pixel density was above 60%, the accuracies of low-pixel-density SSVEP were all satisfactory (>90%) and showed no significant difference with that of the conventional 100%-pixel density. These results support the feasibility of using 60%-pixel density with a square-random presentation mode to improve the comfort of SSVEP-BCI, thereby promoting its practical applications in communication and control.

Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface.

Objective.Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention.Methods.A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection.Results.Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy.Significance.The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.

Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training.

Objective.Deep learning (DL) models have been proven to be effective in decoding motor imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success relies heavily on large amounts of training data, whereas EEG data collection is laborious and time-consuming. Recently, cross-dataset transfer learning has emerged as a promising approach to meet the data requirements of DL models. Nevertheless, transferring knowledge across datasets involving different MI tasks remains a significant challenge in cross-dataset transfer learning, limiting the full utilization of valuable data resources. APPROACH: This study proposes a pre-training-based cross-dataset transfer learning method inspired by Hard Parameter Sharing in multi-task learning. Different datasets with distinct MI paradigms are considered as different tasks, classified with shared feature extraction layers and individual task-specific layers to allow cross-dataset classification with one unified model. Then, Pre-training and fine-tuning are employed to transfer knowledge across datasets. We also designed four fine-tuning schemes and conducted extensive experiments on them. MAIN RESULTS: The results showed that compared to models without pre-training, models with pre-training achieved a maximum increase in accuracy of 7.76%. Moreover, when limited training data were available, the pre-training method significantly improved DL model's accuracy by 27.34% at most. The experiments also revealed that pre-trained models exhibit faster convergence and remarkable robustness. The training time per subject could be reduced by up to 102.83 s, and the variance of classification accuracy decreased by 75.22% at best. SIGNIFICANCE: This study represents the first comprehensive investigation of the cross-dataset transfer learning method between two datasets with different MI tasks. The proposed pre-training method requires only minimal fine-tuning data when applying DL models to new MI paradigms, making MI-Brain-computer interface more practical and user-friendly.

Modality-Attention Promotes the Neural Effects of Precise Timing Prediction in Early Sensory Processing.

Precise timing prediction (TP) enables the brain to accurately predict the occurrence of upcoming events in millisecond timescale, which is fundamental for adaptive behaviors. The neural effect of the TP within a single sensory modality has been widely studied. However, less is known about how precise TP works when the brain is concurrently faced with multimodality sensory inputs. Modality attention (MA) is a crucial cognitive function for dealing with the overwhelming information induced by multimodality sensory inputs. Therefore, it is necessary to investigate whether and how the MA influences the neural effects of the precise TP. This study designed a visual-auditory temporal discrimination task, in which the MA was allocated to visual or auditory modality, and the TP was manipulated into no timing prediction (NTP), matched timing prediction (MTP), and violated timing prediction (VTP) conditions. Behavioral and electroencephalogram (EEG) data were recorded from 27 subjects, event-related potentials (ERP), time-frequency distributions of inter-trial coherence (ITC), and event-related spectral perturbation (ERSP) were analyzed. In the visual modality, precise TP led to N1 amplitude and 200-400 ms theta ITC variations. Such variations only emerged when the MA was attended. In auditory modality, the MTP had the largest P2 amplitude and delta ITC than other TP conditions when the MA was attended, whereas the distinctions disappeared when the MA was unattended. The results suggest that the MA promoted the neural effects of the precise TP in early sensory processing, which provides more neural evidence for better understanding the interactions between the TP and MA.

Modulation of rhythmic visual stimulation on left-right attentional asymmetry.

The rhythmic visual stimulation (RVS)-induced oscillatory brain responses, namely steady-state visual evoked potentials (SSVEPs), have been widely used as a biomarker in studies of neural processing based on the assumption that they would not affect cognition. However, recent studies have suggested that the generation of SSVEPs might be attributed to neural entrainment and thus could impact brain functions. But their neural and behavioral effects are yet to be explored. No study has reported the SSVEP influence on functional cerebral asymmetry (FCA). We propose a novel lateralized visual discrimination paradigm to test the SSVEP effects on visuospatial selective attention by FCA analyses. Thirty-eight participants covertly shifted their attention to a target triangle appearing in either the lower-left or -right visual field (LVF or RVF), and judged its orientation. Meanwhile, participants were exposed to a series of task-independent RVSs at different frequencies, including 0 (no RVS), 10, 15, and 40-Hz. As a result, it showed that target discrimination accuracy and reaction time (RT) varied significantly across RVS frequency. Furthermore, attentional asymmetries differed for the 40-Hz condition relative to the 10-Hz condition as indexed by enhanced RT bias to the right visual field, and larger Pd EEG component for attentional suppression. Our results demonstrated that RVSs had frequency-specific effects on left-right attentional asymmetries in both behavior and neural activities. These findings provided new insights into the functional role of SSVEP on FCAs.

Orthodontically induced external apical root resorption considerations of root-filled teeth vs vital pulp teeth: a systematic review and meta-analysis.

INTRODUCTION: The purpose of this systematic review was to research the difference between root-filled teeth (RFT) and vital pulp teeth (VPT) in orthodontically induced external apical root resorption (EARR) and to offer suggestions for clinicians on therapeutic sequence and timing when considering combined treatment of endodontic and orthodontic. MATERIALS AND METHODS: An electronic search of published studies was conducted before November 2022 in PubMed, Web of Science and other databases. Eligibility criteria were based on the Population, Intervention, Comparison, Outcome, and Study design (PICOS) framework. RevMan 5.3 software was used for statistical analysis. Single-factor meta-regression analysis was used to explore the sources of literature heterogeneity, and a random effects model was used for analysis. RESULTS: This meta-analysis comprised 8 studies with 10 sets of data. As there was significant heterogeneity among the studies, we employed a random effects model. The funnel plot of the random effects model exhibited a symmetrical distribution, indicating no publication bias among the included studies. The EARR rate of RFT was significantly lower than that of VPT. CONCLUSIONS: In the context of concurrent endodontic and orthodontic treatment, priority should be given to endodontic therapy, as it serves as the foundation for subsequent orthodontic procedures. The optimal timing for orthodontic tooth movement post-root canal therapy is contingent upon factors such as the extent of periapical lesion resolution and the degree of dental trauma sustained. A comprehensive clinical assessment is essential in guiding the selection of the most suitable approach for achieving optimal treatment outcomes.

Hierarchical fusion detection algorithm for sleep spindle detection.

Background: Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person's learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The "gold standard" of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. Methods: To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. Results: The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. Conclusion: A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Improving Transfer Performance of Deep Learning with Adaptive Batch Normalization for Brain-computer Interfaces.

Recently, transfer learning and deep learning have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces. However, the generalization ability of these BCIs is still to be further verified in a cross-dataset scenario. This study compared the transfer performance of manifold embedded knowledge transfer and pre-trained EEGNet with three preprocessing strategies. This study also introduced AdaBN for target domain adaptation. The results showed that EEGNet with Riemannian alignment and AdaBN could achieve the best transfer accuracy about 65.6% on the target dataset. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.

Prediction Deviants with Varying Degrees Induce Separable Error-related EEG Features.

Error-related potential (ErrP) usually emerges in the brain when human perceives errors and is believed to be a promising signal for optimizing brain-computer interface (BCI) system. However, most of the ErrP studies only focus on how to distinguish the correct and wrong conditions, which is not enough for the BCI application in real scenarios. Therefore, it is necessary to study the ErrPs induced by the prediction deviants with varying degrees, concurrently test the separability of such EEG features. To this end, electroencephalogram (EEG) data of twelve healthy subjects were recorded when they participated in a direction prediction experiment. There are three prediction -deviant conditions in it, i.e., correct prediction, 90°deviant, 180° deviant. Event-related potential and inter-trial coherence were analyzed. Consequently, the error-related negativity (ERN) and N450 component in FCZ were significantly modulated by the degrees of prediction deviants, especially in the low-frequency band (<13Hz). Moreover, single-trial classification was adopted to test the separability of these features; the averaged accuracies between any two conditions were 87.75%, 85.25%, 64.79%. This study demonstrates the prediction deviants with varying degrees can induce separable ErrP features, which provide a deeper understanding of the ErrP signatures for developing BCIs.

[Classification algorithms of error-related potentials in brain-computer interface].

Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.

Dynamic Brain Responses Modulated by Precise Timing Prediction in an Opposing Process.

The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However, much less is known about how the brain manages the prediction process over time using traditional neuropsychological paradigms. Here, we implemented an innovative paradigm for timing prediction to precisely study the temporal dynamics of neural oscillations. In the experiment recruiting 45 participants, expectation suppression was found for the overall electroencephalographic activity, consistent with previous hemodynamic studies. Notably, we found that N1 was positively associated with predictability while N2 showed a reversed relation to predictability. Furthermore, the matching prediction had a similar profile with no timing prediction, both showing an almost saturated N1 and an absence of N2. The results indicate that the N1 process showed a 'sharpening' effect for predictable inputs, while the N2 process showed a 'dampening' effect. Therefore, these two paradoxical neural effects of prediction, which have provoked wide confusion in accounting for expectation suppression, actually co-exist in the procedure of timing prediction but work in separate time windows. These findings strongly support a recently-proposed opposing process theory.

Timing Prediction Changes the Signatures of Alpha-Band Functional and Effective Connectivity.

Timing prediction plays a key role in optimizing sensory perception and guiding adaptive behaviors. It is critical to study the neural signatures of timing prediction. Comparing to numerous studies focusing on the local brain area, less is known about how the timing prediction influences the functional and effective connectivity of the whole brain network. This study designed a double-tap task, in which the period before the first tap had no timing prediction (NTP), while that of the second tap was influenced by timing prediction (TP). Twelve subjects participated in this study. The functional connectivity was measured by an undirected network constructed by phase-lag index (PLI), while the effective connectivity was measured by a directed network constructed by partial directed coherence (PDC). By comparing the connection strength and modes between NTP and TP, it's found that in alpha-band, timing prediction could improve the global efficiency and transitivity of PLI networks, and shift the in-degree center of PDC networks from frontal area to parieto-occipital area. These results could provide neural evidence for the modeling of timing prediction.

Classification of auditory attention focuses during speech perception.

Passive brain-computer interfaces (BCIs) covertly decode the cognitive and emotional states of users by using neurophysiological signals. An important issue for passive BCIs is to monitor the attentional state of the brain. Previous studies mainly focus on the classification of attention levels, i.e. high vs. low levels, but few has investigated the classification of attention focuses during speech perception. In this paper, we tried to use electroencephalography (EEG) to recognize the subject's attention focuses on either call sign or number when listening to a short sentence. Fifteen subjects participated in this study, and they were required to focus on either call sign or number for each listening task. A new algorithm was proposed to classify the EEG patterns of different attention focuses, which combined common spatial pattern (CSP), short-time Fourier transformation (STFT) and discriminative canonical pattern matching (DCPM). As a result, the accuracy reached an average of 78.38% with a peak of 93.93% for single trial classification. The results of this study demonstrate the proposed algorithm is effective to classify the auditory attention focuses during speech perception.

Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.

Brain-computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.

EEG Complexity and Functional Connectivity During Precise Timing Prediction.

Predictive timing in millisecond-scale is crucial for human beings to efficiently accomplish ongoing perceptions and actions. It is believed that the predictive timing process is an implementation of the predictive coding model in time domain, which can flexibly deal with both the matching and mismatching predictions. However, it's still far from being understood how the neural signature differs between the two conditions during precise timing prediction. As the brain is a complex system, it is necessary to use nonlinear measures and functional connectivity to investigate the brain function of timing prediction. Here, we probe into the EEG signatures during predictive timing process by the sample entropy (SampEn), Lempel-Ziv complexity (LZC) and partial directed coherence methods. Significant lower EEG complexity and stronger brain functional connectivity were observed when the stimulus matches the timing prediction. The current observation may shed light on the modeling of the precise predictive timing process.