Objective . This study aimed to design and evaluate a high-speed online steady-state visually evoked potential (SSVEP)-based brain–computer interface (BCI) in an optical see-through (OST) augmented reality (AR) environment. Approach . An eight-class BCI was designed in an OST-AR headset which is wearable and allows users to see the user interface of the BCI and the device to be controlled in the same view field via the OST head-mounted display. The accuracies, information transfer rates (ITRs), and SSVEP signal characteristics of the AR-BCI were evaluated and compared with a computer screen-based BCI implemented with a laptop in offline and online cue-guided tasks. Then, the performance of the AR-BCI was evaluated in an online robotic arm control task. Main results . The offline results obtained during the cue-guided task performed with the AR-BCI showed maximum averaged ITRs of 65.50 ± 9.86 bits min ⁻¹ according to the extended canonical correlation analysis-based target identification method. The online cue-guided task achieved averaged ITRs of 65.03 ± 11.40 bits min ⁻¹ . The online robotic arm control task achieved averaged ITRs of 45.57 ± 7.40 bits min ⁻¹ . Compared with the screen-based BCI, some limitations of the AR environment impaired BCI performance and the quality of SSVEP signals. Significance . The results showed the potential for providing a high-performance brain–control interaction method by combining AR and BCI. This study could provide methodological guidelines for developing more wearable BCIs in OST-AR environments and will also encourage more interesting applications involving BCIs and AR techniques.

Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to build adaptive aiding systems for human–machine systems by estimating MWL in real time. However, extracting EEG features which are consistent in indicating MWL across different tasks is still one of the critical challenges. This study attempts to compare the cross-task consistency in indexing MWL variations between two commonly used EEG-based MWL indicators, power spectral density (PSD) of ongoing EEG and task-irrelevant auditory ERPs (tir-aERPs). The verbal N-back and the multi-attribute task battery (MATB), both with two difficulty levels, were employed in the experiment, along with task-irrelevant auditory probes. EEG was recorded from 17 subjects when they were performing the tasks. The tir-aERPs elicited by the auditory probes and the relative PSDs of ongoing EEG between two consecutive auditory probes were extracted and statistically analyzed to reveal the effects of MWL and task type. Discriminant analysis and support vector machine were employed to examine the generalization of tir-aERP and PSD features in indexing MWL variations across different tasks. The results showed that the amplitudes of tir-aERP components, N1, early P3a, late P3a, and the reorienting negativity, significantly decreased with the increasing MWL in both N-back and MATB. Task type had no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERP features. The relative PSDs in θ, α, and low β bands were also sensitive to MWL variations. However, the MWL-sensitive PSD features and their topological patterns were significantly affected by task type. The cross-task classification results based on tir-aERP features also significantly outperformed the PSD features. These results suggest that the tir-aERPs should be potentially more consistent MWL indicators across very different task types when compared to PSD. The current study may provide new insights to our understanding of the common and distinctive neuropsychological essences of MWL across different tasks.

Objective:Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency. Methods: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment. Results: The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00±7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency. Significance: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.

Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown. Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification. Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions. The sample size is relatively small. Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.

This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.

The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.