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A new method of environmental control that does not depend on motor functions is eagerly awaited to support independent living for people with severe quadriplegia. In this study, we conducted an exploratory case study of brain state discrimination in a quadriplegic subject to develop a brain-computer interface controlled by a mental task execution. We measured near-infrared spectroscopy (NIRS) signals in a patient with a cervical spinal cord injury while performing mental tasks. A block design with a task and a rest separated by 30 seconds was used to measure brain function. The utilized mental tasks were mental arithmetic and Japanese word chains. Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. The exact binomial test was used for the statistical analysis, and a two-tailed test with a significance level of 5% was performed. The results showed that the number of correct responses was 15 out of 17 (p = 0.002) for the mental arithmetic task and 14 out of 17 (p = 0.013) for the Japanese word chains task, for an overall accuracy of 85%. These results indicate that this method can discriminate the brain state of a patient with quadriplegia from the NIRS signal. By applying these findings to a brain-computer interface, it will be possible to provide a new means of environmental control for individuals with quadriplegia.

A new method of environmental control that does not depend on motor functions is eagerly awaited to support independent living for people with severe quadriplegia. In this study, we conducted an exploratory case study of brain state discrimination in a quadriplegic subject to develop a brain-computer interface controlled by a mental task execution. We measured near-infrared spectroscopy (NIRS) signals in a patient with a cervical spinal cord injury while performing mental tasks. A block design with a task and a rest separated by 30 seconds was used to measure brain function. The utilized mental tasks were mental arithmetic and Japanese word chains. Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. The exact binomial test was used for the statistical analysis, and a two-tailed test with a significance level of 5% was performed. The results showed that the number of correct responses was 15 out of 17 (p = 0.002) for the mental arithmetic task and 14 out of 17 (p = 0.013) for the Japanese word chains task, for an overall accuracy of 85%. These results indicate that this method can discriminate the brain state of a patient with quadriplegia from the NIRS signal. By applying these findings to a brain-computer interface, it will be possible to provide a new means of environmental control for individuals with quadriplegia. [ABSTRACT FROM AUTHOR]

Many current brain–computer interface (BCI) applications depend on the quick processing of brain signals. Most researchers strive to create new methods for future implementation and enhance existing models to discover an optimal feature set that can operate independently. This study focuses on four key concepts that will be used to complete future works. The first concept is related to potential future communication models, whereas the others aim to enhance previous models or methodologies. The four concepts are as follows. First, we suggest a new communication imagery model as a substitute for a speech imager that relies on a mental task approach. As speech imagery is intricate, one cannot imagine the sounds of every character in every language. Our study proposes a new mental task model for lip-sync imagery that can be employed in all languages. Any character in any language can be used with this mental task model. In this study, we utilized two lip-sync movements to indicate two sounds, characters, or letters. Second, we considered innovative hybrid signals. Choosing an unsuitable frequency range can lead to ineffective feature extractions. Therefore, the selection of an appropriate frequency range is crucial for processing. The ultimate goal of this method is to accurately discover distinct frequencies of brain imagery activities. The restricted frequency range combination presents an initial proposal for generating fragmented, continuous frequencies. The first model assesses two 4 Hz intervals as filter banks. The primary objective is to discover new combinations of signals at 8 Hz by selecting filter banks with a 4 Hz scale from the frequency range of 4 Hz to 40 Hz. This approach facilitates the acquisition of efficient and clearly defined features by reducing similar patterns and enhancing distinctive patterns of brain activity. Third, we introduce a new linear bond graph classifier as a supplement to a linear support vector machine (SVM) when handling noisy data. The performance of the linear support vector machine (SVM) significantly declines under high-noise conditions. To complement the linear support vector machine (SVM) in noisy-data situations, we introduce a new linear bond graph classifier. Fourth, this paper presents a deep-learning model for formula recognition that converts the first-layer data into a formula extraction model. The primary goal is to decrease the noise in the formula coefficients of the subsequent layers. The output of the final layer comprises coefficients chosen by different functions at various levels. The classifier then extracts the root interval for each formula, and a diagnosis is established based on these intervals. The final goal of the last idea is to explain the main brain imagery activity formula using a combination formula for similar and distinctive brain imagery activities. The results of implementing all of the proposed methods are reported. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for new combinations of signals. [ABSTRACT FROM AUTHOR]

BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals. [ABSTRACT FROM AUTHOR]

Many current brain–computer interface (BCI) applications depend on the quick processing of brain signals. Most researchers strive to create new methods for future implementation and enhance existing models to discover an optimal feature set that can operate independently. This study focuses on four key concepts that will be used to complete future works. The first concept is related to potential future communication models, whereas the others aim to enhance previous models or methodologies. The four concepts are as follows. First, we suggest a new communication imagery model as a substitute for a speech imager that relies on a mental task approach. As speech imagery is intricate, one cannot imagine the sounds of every character in every language. Our study proposes a new mental task model for lip-sync imagery that can be employed in all languages. Any character in any language can be used with this mental task model. In this study, we utilized two lip-sync movements to indicate two sounds, characters, or letters. Second, we considered innovative hybrid signals. Choosing an unsuitable frequency range can lead to ineffective feature extractions. Therefore, the selection of an appropriate frequency range is crucial for processing. The ultimate goal of this method is to accurately discover distinct frequencies of brain imagery activities. The restricted frequency range combination presents an initial proposal for generating fragmented, continuous frequencies. The first model assesses two 4 Hz intervals as filter banks. The primary objective is to discover new combinations of signals at 8 Hz by selecting filter banks with a 4 Hz scale from the frequency range of 4 Hz to 40 Hz. This approach facilitates the acquisition of efficient and clearly defined features by reducing similar patterns and enhancing distinctive patterns of brain activity. Third, we introduce a new linear bond graph classifier as a supplement to a linear support vector machine (SVM) when handling noisy data. The performance of the linear support vector machine (SVM) significantly declines under high-noise conditions. To complement the linear support vector machine (SVM) in noisy-data situations, we introduce a new linear bond graph classifier. Fourth, this paper presents a deep-learning model for formula recognition that converts the first-layer data into a formula extraction model. The primary goal is to decrease the noise in the formula coefficients of the subsequent layers. The output of the final layer comprises coefficients chosen by different functions at various levels. The classifier then extracts the root interval for each formula, and a diagnosis is established based on these intervals. The final goal of the last idea is to explain the main brain imagery activity formula using a combination formula for similar and distinctive brain imagery activities. The results of implementing all of the proposed methods are reported. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for new combinations of signals.

Using the method of brain network graph to analyze EEG functional connectivity has the problems of threshold selection and ignoring brain dynamic. To solve this problem, This paper proposed a method of using topological dynamic modeling to analyze the EEG functional connectivity matrix, which improved the accuracy of classification and recognition of mental arithmetic tasks. Firstly, it mapped the functional connectivity matrix to an undirected weighted graph. Then it used the persistent Homology Toolbox to construct different complexes and record the different levels of homology features formed in this topological dynamic process to form the persistence diagrams. Finally, it calculated the persistence landscape features as the input feature of the random forest classifier for mental state recognition. In the two task of mental arithmetic state recognition and mental arithmetic quality classification, the proposed algorithm obtained the highest recognition accuracy at 99. 26% and 99. 20%, sensitivity at 97. 87% and 99. 80% and specificity at 99. 78% and 97. 64%, respectively, and accuracy at 66.81% and 66.85% in the cross-individual verification experiment. Experimental results show that the proposed algorithm is fully considered all possible thresholds and effectively extracts the classification information of EEG functional connectivity to implement the automatic recognition of EEG mental arithmetic state. [ABSTRACT FROM AUTHOR]

BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.

The development of Brain-Computer Interfaces (BCI) gathers experts and specialists in various fields, such as engineering, computer science, medicine, or cognitive neuroscience. Each of these disciplines has specific terminology, which makes mutual understanding and research collaboration difficult. The IEEE P2731 working group aims to improve communication between BCI researchers by developing a functional model and standards for terminology that can be used as a common description framework for all the involved knowledge fields. This work focuses on the vocabulary of mental processes involved in BCI communication and describes their role in a Functional Model that considers their influence on BCI performance. Finally, it presents potential uses of the proposed model. [ABSTRACT FROM AUTHOR]

Background: To increase the consultation rate of potential major depressive disorder (MDD) patients, we developed a contact-type fingertip photoplethysmography-based MDD screening system. With the outbreak of SARS-CoV-2, we developed an alternative to contact-type fingertip photoplethysmography: a novel web camera-based contact-free MDD screening system (WCF-MSS) for non-contact measurement of autonomic transient responses induced by a mental task. Methods: The WCF-MSS measures time-series interbeat intervals (IBI) by monitoring color tone changes in the facial region of interest induced by arterial pulsation using a web camera (1920 × 1080 pixels, 30 frames/s). Artifacts caused by body movements and head shakes are reduced. The WCF-MSS evaluates autonomic nervous activation from time-series IBI by calculating LF (0.04–0.15 Hz) components of heart rate variability (HRV) corresponding to sympathetic and parasympathetic nervous activity and HF (0.15–0.4 Hz) components equivalent to parasympathetic activities. The clinical test procedure comprises a pre-rest period (Pre-R; 140 s), mental task period (MT; 100 s), and post-rest period (Post-R; 120 s). The WCF-MSS uses logistic regression analysis to discriminate MDD patients from healthy volunteers via an optimal combination of four explanatory variables determined by a minimum redundancy maximum relevance algorithm: HF during MT (HF MT ), the percentage change of LF from pre-rest to MT (%ΔLF(Pre–R⇒ MT) ), the percentage change of HF from pre-rest to MT (%ΔHF(Pre–R⇒ MT) ), and the percentage change of HF from MT to post-rest (%ΔHF(MT⇒ Post–R) ). To clinically test the WCF-MSS, 26 MDD patients (16 males and 10 females, 20–58 years) were recruited from BESLI Clinic in Tokyo, and 27 healthy volunteers (15 males and 12 females, 18–60 years) were recruited from Tokyo Metropolitan University and RICOH Company, Ltd. Electrocardiography was used to calculate HRV variables as references. Result: The WCF-MSS achieved 73% sensitivity and 85% specificity on 5-fold cross-validation. IBI correlated significantly with IBI from reference electrocardiography (r = 0.97, p < 0.0001). Logit scores and subjective self-rating depression scale scores correlated significantly (r = 0.43, p < 0.05). Conclusion: The WCF-MSS seems a promising contact-free MDD screening apparatus. This method enables web camera built-in smartphones to be used as MDD screening systems. [ABSTRACT FROM AUTHOR]

Mental Workload (MWL) and human performance are widely contributing concepts in human factors. The objective of the current study is to investigate the perceived MWL and human performance during whole-body vibration (WBV) exposure while seated at different backrest angles. Nineteen healthy male participants completed both the NASA-TLX and rating scale mental effort (RSME) after performing two difficulty levels of computerized dual tasks. The participants’ performance was measured in these conditions while seated with a backrest angle of 100 and 120 degrees and exposed to WBV (intensity: 0.5 m/s²; frequency 3-20 Hz) for 5 minutes. No significant effect on performance or perceived MWL (p<0.05) was found when changes were made to the backrest angles. Exposure to WBV under two backrest angles increased mental demand (p=0.04), effort (p=0.03) and frustration (p=0.03) and negatively affected human performance (p<0.05). The present study showed that exposure to WBV could be an important variable for designing work environments that require a high level of performance and mental demand while seated. However, the findings exhibited no association between inclining backrest angle and human performance or perceived MWL. [ABSTRACT FROM AUTHOR]

Purpose: Huntington's disease (HD) is an neurodegenerative genetic disorder with characteristic gait changes. HD also results in a range of cognitive impairments, such as difficulties to divide attention, or simultaneously monitoring two tasks. Aim: The impact of cognitive and/or motor tasks on HD gait has not been fully elucidated. The aim was to examine gait in HD patients while performing motor and/or cognitive dual-task walking. Methods: Gait was measured with and without performing concurrent cognitive and/or motor tasks. Sixteen HD patients and 24 healthy control (HC) subjects performed a self-paced basic walking task, a dual motor, a dual mental and a combined motor and mental task while walking. Results: Base walk gait parameters are significantly different between HD and HC groups. Same is true for motor, mental and combined tasks comparisons of HD and HC subjects. Gait velocity is also significantly reduced in HD compared to HC for all experimental conditions. Comparison of base walk and mental task performance showing differences in cycle time, stride length, double support time and CV of stride length, while base walk to motor task comparison is different only in stride length. No differences were found when motor and combined tasks were compared to mental task in HD patients. Conclusion: Gait pattern in HD patients while performing motor and/or cognitive dual tasks walking is remarkably preserved. Gait parameters are changed in order to reduce possible falls, and lack of differences of dual tasks gait parameters variability is attributed that patients minimizing risk of falling and preserving stability. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore how osteopathic treatments influence certain measurable aspects of stress performance in male athletes. Twenty-two (22) healthy male athletes (21,63 ± 1,41 years), participated in the study and were randomly allocated to either a craniosacral therapy or placebo group with 11 participants receiving a craniosacral therapy session, and the other 11 a placebo session. Pre- and post-intervention psychophysiological correlates of stress reaction (skin conductance - SC, heart rate – HR and respiratory rate – RR) were measured. Stress induced from an arithmetic task resulted in a significant increase in physiological stress markers such as SC, HR and RR in both groups. Over the short term, craniosacral therapy was associated with a physiological relaxation response (a decrease in HR and SC) and an altered HR and SC response during the math task in comparison to the placebo group. At least over the short term, the results of this study indicate that participating in 20-min exposure to craniosacral treatment may benefit participants by decreasing heart rate reactivity and skin conductance reactivity. Further research, with some modifications, using a larger sample is required to determine the effectiveness of craniosacral therapy for immediate stress reduction of athletes. [ABSTRACT FROM AUTHOR]

Background: The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires.Objective: Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated.Methods: The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification.Results: The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2.Conclusion: Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition. [ABSTRACT FROM AUTHOR]

Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis. The nine logistic regression variables are averages of heart rate (HR), high frequency (HF) component of heart rate variability (HRV), and the low frequency (LF)/HF ratio of HRV before, during, and after the mental task. We conducted a clinical test of the proposed system. Participants were 6 MDD patients (4 females and 2 males, aged 23–60 years) from Shizuoka Saiseikai General Hospital psychiatry outpatient unit and 14 healthy volunteers from University of Electro-Communications (6 females and 8 males, aged 21–63 years). The average PPG- and ECG (as a reference)-derived HR, HF and LF/HF were significantly correlated with each other (HR; r = 1.00, p < 0.0001, HF; r = 0.98, p < 0.0001, LF/HF; r = 0.98, p < 0.0001). Leave-one-out cross validation (LOOCV) revealed 83% sensitivity and 93% specificity. The proposed system appears promising for future MDD self-screening at home and are expected to encourage psychiatric visits for potential MDD patients.

Abstract: The autonomic and hormonal reactivity in the conditions of mental stress was studied in 248 children and adolescents aged 9-10 to 14-16 years. Changes in functional load level were evaluated based on the dynamics of heart rate variability (HRV) and the salivary cortisol level. A centile table was composed using the changes in these parameters during mental arithmetic tasks. Based on the table, six types of autonomic and hypothalamic-pituitary-adrenal reactivity were determined and gradations of deviations in various parts of the autonomic nervous system (ANS) refined. The reactivity of the ANS and hypothalamic-pituitary-adrenal axis (HPA-axis) was found to depend on the initial functional state and the age of subjects. In early adolescence, an increased level of psychoemotional stress associated with increased activity of the HPA-axis leads to a decrease in the reaction to mental stress. By an age of 14-16 years, the initial functional state of the ANS and HPA-axis tends to improve and, consequently, the reaction to mental stress is normalized. [ABSTRACT FROM AUTHOR]

12.6% of major depressive disorder (MDD) patients have suicide intent, while it has been reported that 43% of patients did not consult their doctors for MDD, automated MDD screening is eagerly anticipated. Recently, in order to achieve automated screening of MDD, biomarkers such as multiplex DNA methylation profiles or physiological method using near infra-red spectroscopy (NIRS) have been studied, however, they require inspection using 96-well DNA ELIZA kit after blood sampling or significant cost. Using a single-lead electrocardiography (ECG), we developed a high-precision MDD screening system using transient autonomic responses induced by dual mental tasks. We developed a novel high precision MDD screening system which is composed of a single-lead ECG monitor, analogue to digital (AD) converter and a personal computer with measurement and analysis program written by LabView programming language. The system discriminates MDD patients from normal subjects using heat rate variability (HRV)-derived transient autonomic responses induced by dual mental tasks, i.e. verbal fluency task and random number generation task, via linear discriminant analysis (LDA) adopting HRV-related predictor variables (hear rate (HR), high frequency (HF), low frequency (LF)/HF). The proposed system was tested for 12 MDD patients (32 ± 15 years) under antidepressant treatment from Shizuoka Saiseikai General Hospital outpatient unit and 30 normal volunteers (37 ± 17 years) from Tokyo Metropolitan University. The proposed system achieved 100% sensitivity and 100% specificity in classifying 42 examinees into 12 MDD patients and 30 normal subjects. The proposed system appears promising for future HRV-based high-precision and low-cost screening of MDDs using only single-lead ECG. [ABSTRACT FROM AUTHOR]

In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.