Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis.

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.

Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.

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.

Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.

Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.Approach. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance.Main results. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%).Significance. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.

[Effects of transcranial direct current stimulation on event-related potentials of mental rotation].

There are few researches on the modulation effect of transcranial direct current stimulation(tDCS) on complex spatial cognition. Especially, the influence of tDCS on the neural electrophysiological response in spatial cognition is not yet clear. This study selected the classic spatial cognition task paradigm (three-dimensional mental rotation task) as the research object. By comparing the changes in behavior and event-related potentials in different modes of tDCS before, during and after the application of tDCS, this study analyzed the behavioral and neurophysiological effects of tDCS on mental rotation. The comparison between active-tDCS and sham-tDCS showed no statistically significant difference in behavior between different stimulation modes. Still, the changes in the amplitudes of P2 and P3 during the stimulation were statistically significant. Compared with sham-tDCS, the amplitudes of P2 and P3 in active-tDCS mode showed a greater decrease during the stimulation. This study clarifies the influence of tDCS on the event-related potentials of the mental rotation task. It shows that tDCS may improve the brain information processing efficiency during the mental rotation task. Also, this study provides a reference for an in-depth understanding and exploration of the modulation effect of tDCS on complex spatial cognition.

Neurophysiological mechanisms of transcranial alternating current stimulation.

Neuronal oscillations are the primary basis for precise temporal coordination of neuronal processing and are linked to different brain functions. Transcranial alternating current stimulation (tACS) has demonstrated promising potential in improving cognition by entraining neural oscillations. Despite positive findings in recent decades, the results obtained are sometimes rife with variance and replicability problems, and the findings translation to humans is quite challenging. A thorough understanding of the mechanisms underlying tACS is necessitated for accurate interpretation of experimental results. Animal models are useful for understanding tACS mechanisms, optimizing parameter administration, and improving rational design for broad horizons of tACS. Here, we review recent electrophysiological advances in tACS from animal models, as well as discuss some critical issues for results coordination and translation. We hope to provide an overview of neurophysiological mechanisms and recommendations for future consideration to improve its validity, specificity, and reproducibility.

Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.

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.

Neurophysiological markers of depression detection and severity prediction in first-episode major depressive disorder.

OBJECTIVE: Deviant γ auditory steady-state responses (γ-ASSRs) have been documented in some psychiatric disorders. Nevertheless, the role of γ-ASSR in drug-naïve first-episode major depressive disorder (FEMD) patients remains equivocal. This study aimed to examine whether γ-ASSRs are impaired in FEMD patients and predict depression severity. METHODS: Cortical reactivity was assessed in a cohort of 28 FEMD patients relative to 30 healthy control (HC) subjects during an ASSR paradigm randomly presented at 40 and 60 Hz. Event-related spectral perturbation and inter-trial phase coherence (ITC) were calculated to quantify dynamic changes of the γ-ASSR. Receiver operating characteristic curve combined with binary logistic regression were then employed to summarize ASSR variables that maximally differentiated groups. RESULTS: FEMD patients exhibited significantly inferior 40 Hz-ASSR-ITC in the right hemisphere versus HC subjects (p = 0.007), along with attenuated θ-ITC that reflected underlying impairments in θ responses during 60 Hz clicks (p < 0.05). Moreover, the 40 Hz-ASSR-ITC and θ-ITC in the right hemisphere can be used as a combinational marker to detect FEMD patients with 84.0 % sensitivity and 81.5 % specificity (area under the curve was 0.868, 95 % CI: 0.768-0.968). Pearson's correlations between the depression severity and ASSR variables were further conducted. The symptom severity of FEMD patients was negatively correlated with 60 Hz-ASSR-ITC in the midline and right hemisphere, possibly indicating that depression severity mediated high γ neural synchrony. CONCLUSIONS: Our findings provide critical insight into the pathological mechanism of FEMD, suggesting first that 40 Hz-ASSR-ITC and θ-ITC in right hemisphere constitute potential neurophysiological markers for early depression detection, and second, that high γ entrainment deficits may contribute to underlying symptom severity in FEMD patients.

Altered fronto-central theta-gamma coupling in major depressive disorder during auditory steady-state responses.

OBJECTIVE: Neural oscillations during sensory and cognitive events interact at different frequencies. However, such evidence in major depressive disorder (MDD) remains scarce. We explored the possible abnormal neural oscillations in MDD by analyzing theta-phase/gamma-amplitude coupling (TGC). METHODS: Resting-state and auditory steady-state response (ASSR) electroencephalography recordings were obtained from 35 first-episode MDD and 35 healthy controls (HCs). TGC during rest, ASSR stimulation, and ASSR baseline between and within groups were analyzed to evaluate MDD alterations. Receiver operating characteristic (ROC), TGC comparison between MDD severity subgroups (mild, moderate, major), and correlations were investigated to determine the potential use of altered TGC for identifying MDD. RESULTS: In MDD, left fronto-central TGC decreased during stimulation, while right fronto-central TGC increased during baseline. The area under ROC curve for altered TGC was 0.863. Furthermore, during stimulation, moderate and major MDD groups exhibited significantly lower TGC than mild group, and fronto-central TGC was negatively correlated with depression scale scores. CONCLUSIONS: Our results provided the first evidence for an abnormal TGC response of fronto-central regions in MDD during an ASSR task. Importantly, altered TGC may be promising biomarkers of MDD. SIGNIFICANCE: Our findings enhance the understanding of physiological mechanisms underlying MDD and aid in its clinical diagnosis.

Depression recognition using a proposed speech chain model fusing speech production and perception features.

BACKGROUND: Increasing depression patients puts great pressure on clinical diagnosis. Audio-based diagnosis is a helpful auxiliary tool for early mass screening. However, current methods consider only speech perception features, ignoring patients' vocal tract changes, which may partly result in the poor recognition. METHODS: This work proposes a novel machine speech chain model for depression recognition (MSCDR) that can capture text-independent depressive speech representation from the speaker's mouth to the listener's ear to improve recognition performance. In the proposed MSCDR, linear predictive coding (LPC) and Mel-frequency cepstral coefficients (MFCC) features are extracted to describe the processes of speech generation and of speech perception, respectively. Then, a one-dimensional convolutional neural network and a long short-term memory network sequentially capture intra- and inter-segment dynamic depressive features for classification. RESULTS: We tested the MSCDR on two public datasets with different languages and paradigms, namely, the Distress Analysis Interview Corpus-Wizard of Oz and the Multi-modal Open Dataset for Mental-disorder Analysis. The accuracy of the MSCDR on the two datasets was 0.77 and 0.86, and the average F1 score was 0.75 and 0.86, which were better than the other existing methods. This improvement reveals the complementarity of speech production and perception features in carrying depressive information. LIMITATIONS: The sample size was relatively small, which may limit the application in clinical translation to some extent. CONCLUSION: This experiment proves the good generalization ability and superiority of the proposed MSCDR and suggests that the vocal tract changes in patients with depression deserve attention for audio-based depression diagnosis.

Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. LIMITATION: The sample size is relatively small. CONCLUSION: 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.

Altered gamma oscillations and beta-gamma coupling in drug-naive first-episode major depressive disorder: Association with sleep and cognitive disturbance.

OBJECTIVE: Gamma oscillations contribute to the pathogenesis mechanisms of major depressive disorder (MDD) have been proposed, but gamma activity is not well characterized. This study is the first attempt to investigate the altered gamma oscillations in first-episode MDD, particularly the beta-gamma coupling, and to determine the potential symptomatic relationship with the identified gamma dysregulation. METHODS: Resting electroencephalography was recorded for 43 drug-naive first-episode MDD and 57 healthy control (HC) subjects. Integrated analysis of relative spectral power, weighted phase lag index, and phase-amplitude coupling (PAC) were utilized to reveal the alterations of gamma activities. Pearson's correlation was implemented to identify the relationship between altered gamma activities and the clinical depressive symptoms, which were categorized into four factors: anxiety somatization, retardation, cognitive disturbance, and sleep disturbance. RESULTS: Compared with HC subjects, MDD patients showed not only significantly decreased gamma powers in the left temporal and the bilateral occipital regions but also weakened gamma connectivity between the left hemisphere and the right frontal region. Furthermore, attenuated beta-gamma PAC of MDD patients was observed in the left temporal regions. Importantly, the suppression of left occipital mid- and high gamma oscillations were negatively correlated with sleep disturbance, while the deficits in left temporal beta-mid-gamma PAC and beta-high gamma PAC showed negative correlations with cognitive disturbance. LIMITATIONS: Important limitations are the small sample size and the possible inclusion of bipolar depression in the MDD group. CONCLUSIONS: Our findings provide the first evidence that in first-episode MDD, aberrant gamma powers and beta-gamma coupling are associated with sleep and cognitive impairments, respectively, deepening our understanding of the physiological mechanisms underlying sleep and cognitive symptoms in first-episode MDD. Altered gamma oscillations emerge as promising biomarkers for diagnosing MDD.

Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Reduced Attention Bias Toward Negative Facial Expression: A Pilot Study in Healthy Subjects.

Research in the cognitive neuroscience field has shown that individuals with a stronger attention bias for negative information had higher depression risk, which may be the underlying pathogenesis of depression. This dysfunction of affect-biased attention also represents a decline in emotion regulation ability. Clinical studies have suggested that transcranial direct current stimulation (tDCS) treatment can improve the symptoms of depression, yet the neural mechanism behind this improvement is still veiled. This study aims to investigate the effects of tDCS on affect-biased attention. A sample of healthy participants received 20 min active (n = 22) or sham tDCS (n = 19) over the left dorsolateral prefrontal cortex (DLPFC) for 7 consecutive days. Electroencephalographic (EEG) signals were recorded while performing the rest task and emotional oddball task. The oddball task required response to pictures of the target (positive or negative) emotional facial stimuli and neglecting distracter (negative or positive) or standard (neutral) stimuli. Welch power spectrum estimation algorithm was applied to calculate frontal alpha asymmetry (FAA) in the rest task, and the overlapping averaging method was used to extract event-related potentials (ERP) components in the oddball task. Compared to sham tDCS, active tDCS caused an obvious increment in FAA in connection with emotion regulation (p < 0.05). Also, participants in the active tDCS group show greater P3 amplitudes following positive targets (p < 0.05) and greater N2 amplitudes following negative distracters (p < 0.05), reflecting emotion-related attention biases. These results offer valuable insights into the relationship between affect-biased attention and the effects of tDCS, which may be of assistance in exploring the neuropathological mechanism of depression and anxiety and new treatment strategies for tDCS.

Discriminating subclinical depression from major depression using multi-scale brain functional features: A radiomics analysis.

BACKGROUND: The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy. METHODS: Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). A novel radiomics analysis was proposed to discriminate SD from MD. Multi-scale brain functional features were extracted to explore a comprehensive representation of functional characteristics. A two-level feature selection strategy and support vector machine (SVM) were employed for classification. RESULTS: The overall classification accuracy among SD, MD and HC groups was 84.21%. Particularly, the model excellently distinguished SD from MD with 96.77% accuracy, 100% sensitivity, and 92.31% specificity. Moreover, features with high discriminative power to distinguish SD from MD showed a strong association with default mode network, frontoparietal network, affective network, and visual network regions. LIMITATION: The sample size was relatively small, which may limit the application in clinical translation to some extent. CONCLUSION: These findings demonstrated that a valid radiomics approach based on functional measures can discriminate SD from MD with a high classification performance, facilitating an objective and reliable diagnosis individually in clinical practice. Features with high discriminative power may provide insight into a profound understanding of the brain functional impairments and pathophysiology of SD and MD.

Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials.

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 ß 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.

Optimization of Task Allocation for Collaborative Brain-Computer Interface Based on Motor Imagery.

OBJECTIVE: Collaborative brain-computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy's. APPROACH: This study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject's instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called "2 Tasks" … "5 Tasks." To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI. MAIN RESULTS: Taking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at "4 Tasks," which means each subset contained four instructions. It outperformed the common-work strategy ("6 Tasks") in classification accuracy (72.29 ± 4.43 vs. 58.53 ± 4.36%). SIGNIFICANCE: The results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload.

Acoustoelectric Signal Decoding Based on Fourier Approximation.

The acoustoelectric (AE) effect is that ultrasonic wave causes the conductivity of electrolyte to change in local position. AE imaging is an imaging method that utilizes AE effect. The decoding accuracy of AE signal is of great significance to improve the decoded signal quality and resolution of AE imaging. At present, the envelope function is adopted to decode AE signal, but the timing characteristics of the decoded signal and the source signal are not very consistent. In order to further improve the decoding accuracy, based on envelope decoding, the decoding process of AE signal is investigated. Considering with the periodic property of AE signal in time series, the upper envelope signal is further fitted by Fourier approximation. Phantom experiment validates the feasibility of AE signal decoding by Fourier approximation. And the time sequence diagram decoded with envelope is also compared. The fitted curve can represent the overall trend curve of low-frequency current signal, which has a significant correspondence with the current source signal. The main performance is of the same frequency and phase. Experiment results validate that the proposed decoding algorithm can improve the decoding accuracy of AE signal and be of potential for the clinical application of AE imaging.

Task-irrelevant Auditory Event-related Potentials as Mental Workload Indicators: A Between-task Comparison Study.

Real-time monitoring of mental workload (MWL) is a crucial step to build closed-loop adaptive aiding systems for human-machine systems. MWL estimators based on spontaneous electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to achieve this goal. However, the previous studies show that the between-task robustness of these EEG/ERP-based MWL estimators is still an unsolved intractable question. This study attempts to examine the task-irrelevant auditory event-related potentials (tir-aERPs) as MWL indicators. A working memory task (verbal n-back) and a visuo-motor task (multi-attribute task battery, MATB), both with two difficulty levels (easy and hard), were used in the experiment, along with task-irrelevant auditory probes that did not need any response from the participants. EEG was recorded from ten participants when they were performing the tasks. The tir-aERPs elicited by the auditory probes were extracted and analyzed. The results show that the amplitudes of N1, early P3a (eP3a) and the late reorienting negativity (RON) significantly decreased with the increasing MWL in both n-back and MATB. Task type has no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERPs features. These results suggest that the tir-aERPs are potentially more constant MWL indicators across very different task types. Therefore, the tir-aERPs should be taken into consideration in future task-independent MWL monitoring studies.

Effects of HD-tDCS combined with working memory training on event-related potentials.

Transcranial direct current stimulation (tDCS) provides a non-invasive approach to modulate brain functions. Some studies have shown that tDCS combined with working memory training can alter the effect of training. This study aims to investigate the effect of HD-tDCS over the left dorsolateral prefrontal cortex combined with N-back task on the amplitude of event related potentials (ERP). In the experiment, subjects performed N-back training for 30min every day with active or sham tDCS for 10 days. EEG data were recorded when subjects performing N-back tests prior to the training, 1 day and 20 days post the training, respectively. With the analyses of ERP components, it was found that there were no significant differences between active and sham groups. However, the results of post-test were significantly different from the pre-test. Subsequently, both in active group and in sham group, the amplitude of ERP increased in the frontoparietal and occipital regions 1 day post training. Those alterations were enhanced 20 days post training in the active group but not in the sham group. The results indicated the aftereffect of HD-tDCS to promote the effects of cognitive training, showing accumulative positive aftereffects on ERP 20 days after the stimulation.