Measuring aviator workload using EEG: an individualized approach to workload manipulation.

Introduction: Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience. Methods: Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance. Results: Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload. Discussion: The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.

Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.

Study on the Effect of Dalbergia pinnata (Lour.) Prain Essential Oil on Electroencephalography upon Stimulation with Different Auditory Effects.

Dalbergia pinnata (Lour.) Prain (D. pinnata) is a valuable medicinal plant, and its volatile parts have a pleasant aroma. In recent years, there have been a large number of studies investigating the effect of aroma on human performance. However, the effect of the aroma of D. pinnata on human psychophysiological activity has not been reported. Few reports have been made about the effects of aroma and sound on human electroencephalographic (EEG) activity. This study aimed to investigate the effects of D. pinnata essential oil in EEG activity response to various auditory stimuli. In the EEG study, 30 healthy volunteers (15 men and 15 women) participated. The electroencephalogram changes of participants during the essential oil (EO) of D. pinnata inhalation under white noise, pink noise and traffic noise stimulations were recorded. EEG data from 30 electrodes placed on the scalp were analyzed according to the international 10-20 system. The EO of D. pinnata had various effects on the brain when subjected to different auditory stimuli. In EEG studies, delta waves increased by 20% in noiseless and white noise environments, a change that may aid sleep and relaxation. In the presence of pink noise and traffic noise, alpha and delta wave activity (frontal pole and frontal lobe) increased markedly when inhaling the EO of D. pinnata, a change that may help reduce anxiety. When inhaling the EO of D. pinnata with different auditory stimuli, women are more likely to relax and get sleepy compared to men.

Classification of cognitive impairment using electroencephalography for clinical inspection.

Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

Prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients-A retrospective observational study.

OBJECTIVE: Electroencephalographic (EEG) abnormalities especially non-convulsive status epilepticus (NCSE) have been found to be associated with worse outcomes in critically ill patients. We aimed to assess the prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients. Furthermore, we aimed to investigate any association between the type of EEG abnormality and outcomes including ICU mortality and successful ICU discharge. METHODS: This was a cross-sectional observational study carried out among critically ill patients in a mixed medical-surgical ICU from January 1, 2018 to May 15, 2020. A total of 178 records of 30 min bedside EEG records were found. EEG findings were grouped as normal, non-convulsive seizures (NCS), non-convulsive status epilepticus (NCSE), and other abnormalities. Descriptive analytical tools were used to characterize the case details in terms of the type of EEG abnormalities. Chi square test was used to describe the EEG abnormalities in terms of mortality. The status epilepticus severity scores (STESS) were further calculated for records with NCSE. These data were then analyzed for any association between STESS and mortality for cases with NCSE. RESULTS: The prevalence of EEG abnormality in our cohort of all critically ill patients was found to be 7.3% (170/2234). Among the patients with altered sensorium in whom EEG was done, 42.9% had non-conclusive seizure activity with 25.2% in NCSE. Though the study was not adequately powered, there was a definite trend towards a lower proportion of successful ICU discharge rates seen among patients with higher STESS (>2) with only 33.3% being discharged for patients with a STESS of 6 versus 92.9% for those with STESS 3. SIGNIFICANCE: When combined with a strong clinical suspicion, even a 30-min bedside EEG can result in detection of EEG abnormalities including NCS and NCSE. Hence, EEG should be regularly included in the evaluation of critically ill patients with altered sensorium. PLAIN LANGUAGE SUMMARY: Electroencephalographic (EEG) abnormalities and seizures can have high prevalence in critically ill patients. These abnormalities notably, non-convulsive status epilepticus (NCSE) has been found to be associated with poor patient outcomes. This was a retrospective observational study analyzing 178 EEG records, from a mixed medical-surgical ICU. The indication for obtaining an EEG was based solely on the clinical suspicion of the treating physician. The study found a high prevalence of EEG abnormalities in 96.5% in whom it was obtained with 42.9% having any seizure activity and 28.8% having NCSE. The study was not powered for detection of association of the EEG abnormalities with clinical outcomes. However, a definite trend towards decreased chances of successful discharge from the ICU was seen. This study used strong clinical suspicion in patients with altered sensorium to obtain an EEG. High detection rates of EEG abnormalities were recorded in this study. Hence, combination of clinical judgement and EEG can improve detection of EEG abnormalities and NCSE.

Brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed-loop brain-computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom report. METHODS: Randomization to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1-month data also were examined. RESULTS: The BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, -2.60 vs. 0.38; 95% confidence interval, -3.67, -1.46 [p = .000; effect size, 1.07]; PLC, -2.26; 95% confidence interval, -3.33, -1.19 [p = .001 vs. WLC; effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group only. CONCLUSIONS: BCI is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long-term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the intervention. PLAIN LANGUAGE SUMMARY: Chemotherapy-induced nerve pain (neuropathy) can be disabling for cancer survivors; however, the way symptoms are felt depends on how the brain interprets the signals from nerves in the body. We determined that the perception of neuropathy can be changed by working directly with the brain. Survivors in our trial played 20 sessions of a type of video game that was designed to change the way the brain processed sensation and movement. In this, our second trial, we again observed significant improvement in symptoms that lasted after the treatment was complete.

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification.

Introduction: Motor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain. Methods: To achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations. Results and discussion: Empirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.

Method for analyzing sequential services using EEG: Micro-meso analysis of emotional changes in real flight service.

Capturing customers' emotional changes in sequential service should be realized using physiological measurements to assess customer delight. Questionnaire-based customer surveys may miss significant and dissipating emotional responses. This study developed a micro­meso analysis method of capturing emotional changes for sequential service using electroencephalograph (EEG) measurement, dealing with both service encounters (micro-level) and servicescape (meso­level) over a couple of hours. Customers' emotion states were defined based on emotional arousal and valence. Emotional responses caused by human interactions were evaluated, and periods of high positive affect throughout the customer journey were visualized. Experiments in actual flight services demonstrated successful emotion estimation across flight phases using a single-channel EEG measurement over two hours. Analysis results on the measurement data revealed emotional peaks outside service encounters that are not captured in customers' individual self-reports. The results also statistically revealed that two individual services (asking about a refill and conversations started by flight attendants) evoked high positive affect. Temporal dynamic analyses around high positive affect suggested patterns of interplay between joy and surprise, which are key components of customer delight. Compared with questionnaire-based evaluation, the proposed method contributes significantly to empirical studies on sequential services in marketing and design by enabling the extraction of "high positive affect," which needs to be identified for customer delight. This study supplements existing research on the interactions among physiology (EEG), behavior (emotional changes), and customer service research.

Correlation of Various Sleep Patterns on Different Types of Memory Retention: A Systematic Review.

Sleep has a substantial impact on memory consolidation, although the link between specific sleep patterns and different forms of memory retention is not well-understood. The purpose of this systematic review is to investigate the correlation between varying sleep habits and memory recall. To identify pertinent research published between 2017 and 2023, a thorough check of electronic databases was carried out. Inclusion criteria encompassed peer-reviewed articles published in English, focusing on human participants, and investigating the relationship between sleep patterns and memory retention. Data extraction and quality assessment were performed on selected studies. This research used different strategies and examined several forms of memory retention, including declarative memory, procedural memory, and emotional memory. Several sleep patterns, including sleep duration, sleep stages, and sleep continuity, were investigated. This comprehensive study demonstrated the relationship between adequate sleep duration and memory consolidation, particularly in regard to declarative memory. Furthermore, deep sleep, characterized by slow-wave sleep (SWS), has been associated with superior procedural memory retention. Sleep continuity, as evaluated by reduced sleep fragmentation or undisturbed sleep, influenced memory consolidation across multiple categories of memory. However, the relationship between rapid eye movement (REM) sleep and memory retention remains inconclusive due to conflicting findings. This systematic review emphasizes the significance of various sleep patterns in memory retention. Memory consolidation corresponds with adequate sleep length, deep sleep (or SWS), and sleep continuity. Future research ought to investigate the connection between REM sleep and memory retention. Understanding the impact of specific sleep patterns on memory processes might help guide therapies and interventions to improve memory consolidation and overall cognitive functioning.

Diversity of electroencephalographic patterns during propofol-induced burst suppression.

Burst suppression is a brain state consisting of high-amplitude electrical activity alternating with periods of quieter suppression that can be brought about by disease or by certain anesthetics. Although burst suppression has been studied for decades, few studies have investigated the diverse manifestations of this state within and between human subjects. As part of a clinical trial examining the antidepressant effects of propofol, we gathered burst suppression electroencephalographic (EEG) data from 114 propofol infusions across 21 human subjects with treatment-resistant depression. This data was examined with the objective of describing and quantifying electrical signal diversity. We observed three types of EEG burst activity: canonical broadband bursts (as frequently described in the literature), spindles (narrow-band oscillations reminiscent of sleep spindles), and a new feature that we call low-frequency bursts (LFBs), which are brief deflections of mainly sub-3-Hz power. These three features were distinct in both the time and frequency domains and their occurrence differed significantly across subjects, with some subjects showing many LFBs or spindles and others showing very few. Spectral-power makeup of each feature was also significantly different across subjects. In a subset of nine participants with high-density EEG recordings, we noted that each feature had a unique spatial pattern of amplitude and polarity when measured across the scalp. Finally, we observed that the Bispectral Index Monitor, a commonly used clinical EEG monitor, does not account for the diversity of EEG features when processing the burst suppression state. Overall, this study describes and quantifies variation in the burst suppression EEG state across subjects and repeated infusions of propofol. These findings have implications for the understanding of brain activity under anesthesia and for individualized dosing of anesthetic drugs.

Consequences of scarcity: the impact of perceived scarcity on executive functioning and its neural basis.

Introduction: Previous studies have found a causal relationship between scarcity and the adverse impact it has on executive functioning. However, few studies have directly examined perceived scarcity, and cognitive flexibility (the third component of executive functions) has rarely been included. Methods: Using a 2 (group: scarcity group vs. control group) × 2 (trial type: repeat trial vs. switch trial) mixed design, this study directly explored perceived scarcity's impact on cognitive flexibility and revealed its neural basis in the switching tasks. Seventy college students participated in this study through open recruitment in China. A priming task was used to induce perceived scarcity, thus exploring the impact of perceived scarcity on participants' performance in switching tasks and enabling the analysis of the neural activity of the brain, combined with electroencephalograph (EEG) technology. Results: In terms of behavioral outcomes, perceived scarcity led to poorer performance and a greater switching cost of reaction time in the switching tasks. Regarding neural activity, perceived scarcity led to an increase in the amplitude of P3 differential wave (repeat trials minus switch trials) in the parietal cortex during the target-locked epochs in the switching tasks. Discussion: Perceived scarcity can lead to changes in the neural activity of the brain regions related to executive functioning, resulting in a temporary decrease in cognitive flexibility. It may lead to individuals unable to adapt well to the changing environment, unable to quickly devote themselves to new tasks, and reduce work and learning efficiency in daily life.

Subject-independent EEG classification based on a hybrid neural network.

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.

MI-CAT: A transformer-based domain adaptation network for motor imagery classification.

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain-computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer's self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain-computer interfaces (BCIs).

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study.

Background Meditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explore the effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifier methods that can effectively discriminate between the groups. Materials and methods EEG data were collected from 34 participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. Results The classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, and accuracy from 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. Conclusion This is the first study to our knowledge employing machine learning for the classification among HM meditators and NM The results indicated that machine learning classifiers with EEG functional connectivity as a feature vector could be a viable marker for accessing meditation ability.

Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?

So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.

A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements.

In motor imagery (MI) brain-computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time-frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time-frequency-space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.

The Influence of Noise Exposure at Different Loudness Levels on EEG Index and Types of Attention.

Bachground: Noise is one of the most important harmful factors in the environment. There are limited studies on the effect of noise loudness on brain signals and attention. The main objective of this study was to investigate the relationship between exposure to different loudness levels with brain index, types of attention, and subjective evaluation. Methods: Four noises with different loudness levels were generated. Sixty-four male students participated in this study. Each subject performed the integrated visual and auditory continuous performance test (IVA-2) test before and during exposure to noise loudness signals while their electroencephalography was recorded. Finally, the alpha-to-gamma ratio (AGR), five types of attention, and the subjective evaluation results were examined. Results: During exposure to loudness levels, the AGR and types of attention decreased while the NASA-Tax Load Index (NASA-TLX) scores increased. The noise exposure at lower loudness levels (65 and 75 phon) leads to greater attention dysfunction than at higher loudness. The AGR was significantly changed during exposure to 65 and 75 phon and audio stimuli. This significant change was observed in exposure at all loudness levels except 85 phon and visual stimuli. The divided and sustained attention changed significantly during exposure to all loudness levels and visual stimuli. The AGR had a significant inverse correlation with the total score of NASA-TLX during noise exposure. Conclusions: These results can lead to the design of methods to control the psychological effects of noise at specific frequencies (250 and 4000 Hz) and can prevent non-auditory damage to human cognitive performance in industrial and urban environments.

Brain network mechanism of Xingnao Kaiqiao acupuncture treatment for stroke / 国际中医中药杂志

Objective:To explore the effect of Xingnao Kaiqiao acupuncture treatment on brain network reorganization for the patients with stroke recovery, and therefore understand the neural mechanism underlying Xingnao Kaiqiao acupuncture treatment.Methods:Prospective case series study. Thirteen acute ischemia stroke patients were recruited from the Department of Neurology, Shanghai Minhang Hospital of Integrated Traditional Chinese and Western Medicine from Aug 2018 to Oct 2019. They were treated with Xingnao Kaiqiao acupuncture once a day for 10 consecutive days in addition to routine treatments, and received clinical assessments before treatment and 14 days after treatment onset. EEG signals were recorded during the first acupuncture treatment, from before inserting the needles (the baseline), during needle retention, to after removal of the needles. The brain network was constructed using phase locking index, and its clustering coefficient (CC), characteristic path length (PL) and small-worldness (S) were analyzed using one-way repeated ANOVA.Results:Compared with the baseline, the CC of delta-band network (sparsity=0.10: t=3.306, P=0.006; 0.12: t=2.909, P=0.013; 0.14: t=2.331, P=0.038) and the PL of delta-band (sparsity=0.12: t=3.236, P=0.007; 0.14: t=2.754, P=0.017, 0.18: t=2.878, P=0.014) and alpha-band (sparsity=0.10: t=2.432, P=0.032) networks were significantly decreased during the needle retention stage. Clinical assessments demonstrated a significant treatment efficacy of Xingnao Kaiqiao acupuncture, and its efficacy which was indicated by improved NIHSS score, was significantly correlated with the CC changes in the delta band network from baseline to needle retention. The correlation was strongest when the network sparsity was 0.12 ( r=0.78, P=0.002). Conclusion:Xingnao Kaiqiao acupuncture can regulate the brain network of stroke patients in real time, and this immediate regulation maybe associated with its treatment effect.

Human Error Prediction Using Heart Rate Variability and Electroencephalography.

As human's simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks.