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Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) can modulate human brain dynamics and cognition. However, these modalities have not been compared using multiple imaging techniques concurrently. In this study, 15 participants participated in an experiment involving two sessions with a gap of 10 days. In the first and second sessions, tACS and tDCS were administered to the participants. The anode for tDCS was positioned at point FpZ, and four cathodes were positioned over the left and right prefrontal cortices (PFCs) to target the frontal regions simultaneously. tDCS was administered with 1 mA current. tACS was supplied with a current of 1 mA (zero-to-peak value) at 10 Hz frequency. Stimulation was applied concomitantly with functional near-infrared spectroscopy and electroencephalography acquisitions in the resting-state. The statistical test showed significant alteration (p0.001) in the mean hemodynamic responses during and after tDCS and tACS periods. Between-group comparison revealed a significantly less (p0.001) change in the mean hemodynamic response caused by tACS compared with tDCS. As hypothesized, we successfully increased the hemodynamics in both left and right PFCs using tDCS and tACS. Moreover, a significant increase in alpha-band power (p0.01) and low beta band power (p0.05) due to tACS was observed after the stimulation period. Although tDCS is not frequency-specific, it increased but not significantly (p0.05) the powers of most bands including delta, theta, alpha, low beta, high beta, and gamma. These findings suggest that both hemispheres can be targeted and that both tACS and tDCS are equally effective in high-definition configurations, which may be of clinical relevance.

Background: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Objective: This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. Methods: In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. Results: There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. Conclusion: The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

Brain-computer interface (BCI) technology represents a fast-growing field of research and applications for disabled and healthy people, which is a direct communication pathway to translate the neural information into an active command. Owing to the complicated headset structure, low accuracies, extended training periods, and nonstationary noises, BCI still has many challenges that should be dealt with for further facilitation of BCI technology use in daily life. In this study, a simplified synchronized hybrid BCI system is proposed for multiple command control by the electroencephalograph (EEG) signals in the motor cortex. This system can detect the single motor imagery (MI) task, single steady-state visually evoked potential (SSVEP) task, and hybrid MI + SSVEP tasks simultaneously (total ten mental tasks) via 2 EEG channels with high accuracy. The fast independent component analysis algorithm is employed to hybrid signals for obtaining clear EEG signals resulting from denoising. Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. Finally, the hybrid MI + SSVEP system with a simple structure achieves a high accuracy of 95.56%. Additionally, the single MI-based and the SSVEP-based BCI system obtain the classification accuracy of 90.16% and 93.21%, respectively. Experimental results indicate that the synchronized hybrid BCI system could achieve multiple command control with a simple structure. In comparison with the single MI-based and the SSVEP-based BCI system, the hybrid MI + SSVEP BCI system shows a stable performance and higher efficiency. The proposed investigation provides a new method for the multiple command control by a hybrid BCI system. Also, the proposed BCI system offers the possibility of friendly utilization for disabled people because of its reliability, ease of use, and simplified headset structure.

Ribosome biogenesis is a fundamental and highly orchestrated process that involves hundreds of ribosome biogenesis factors. Despite advances that have been made in yeast, the molecular mechanism of ribosome biogenesis remains largely unknown in plants. We uncovered a WD40 protein, Shrunken and Embryo Defective Kernel 1 (SHREK1), and showed that it plays a crucial role in ribosome biogenesis and kernel development in maize (Zea mays). The shrek1 mutant shows an aborted embryo and underdeveloped endosperm and embryo-lethal in maize. SHREK1 localizes mainly to the nucleolus and accumulates to high levels in the seed. Depleting SHREK1 perturbs pre-rRNA processing and causes imbalanced profiles of mature rRNA and ribosome. The expression pattern of ribosomal-related genes is significantly altered in shrek1. Like its yeast (Saccharomyces cerevisiae) ortholog Periodic tryptophan protein 1 (PWP1), SHREK1 physically interacts with ribosomal protein ZmRPL7a, a transient component of the PWP1-subcomplex involved in pre-rRNA processing in yeast. Additionally, SHREK1 may assist in the A3 cleavage of the pre-rRNA in maize by interacting with the nucleolar protein ZmPOP4, a maize homolog of the yeast RNase mitochondrial RNA-processing complex subunit. Overall, our work demonstrates a vital role of SHREK1 in pre-60S ribosome maturation, and reveals that impaired ribosome function accounts for the embryo lethality in shrek1.

BackgroundBrain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.ObjectiveThis study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.MethodA systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.ResultsSixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.ConclusionAn appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

Mild cognitive impairment (MCI) is an intermediate stage leading to Alzheimer's disease (AD). Diagnosis for MCI patients at an early stage can reduce the chances of developing into a severe condition for cognition. This study aims to identify the healthy control (HC) and MCI through the neural images in the specific time points during the mental tasks by the convolutional neural network (CNN). The signals were acquired by the functional near-infrared spectroscopy (fNIRS). 15 MCI patients and 9 HC subjects are employed in the experiment to perform the N-back task, Stroop task, and verbal fluency task (VFT), respectively. The neural images were generated by the brain map in the specific time points (i.e., 5 sec, 10 sec, 15 sec, 20 sec, 25 sec, 30 sec, 35 sec, 40 sec, 45 sec, 50 sec, 55 sec, 60 sec, and 65 sec). Four layers CNN were applied to classify the neural images of the different time points for three mental tasks (i.e., N-back, Stroop, and VFT). For evaluating the performance of the classifier, we utilized a 5-fold cross-validation method. The CNN results indicated that all the mental tasks obtained a great performance (i.e., averaged accuracy of N-back: 82.59%, Stroop: 85.03%, and VFT: 82.20%). Especially, the highest accuracy of the Stroop task in the 60-sec time point is 98.57%. Thus, these findings demonstrate that the neural images can be useful for the identification of the MCI. The fNIRS could be a next promising non-invasive neural imaging tool for early detection of AD in the clinical field.

Objective: This study investigates a new hand exoskeleton control framework for stroke patients using a brain-computer interface (BCI) technique. Methods: Hand rehabilitation exoskeletons are required to improve critical features, including portability, low cost, safe human-robotic interaction, and intelligent control. The new hand exoskeleton works on brain-computer interface techniques to present a feasibility human-control framework, further to evaluate stroke patients in rehabilitation action research. A visualization design software is utilized to construct the mechatronic system of the hand exoskeleton. A subscript process in the software sets motion parameters of torque, angular displacement, and angular velocity to present a feasibility aim in extension and flexion motion. Main results: The simulation results showed that the proposed hand exoskeleton could generate enough range in finger extension and flexion motion without any interruptions, and real motion experiments by the 3D product also support the feasibility result. Significant: The hand exoskeleton can be used to explore stroke patients in cortex hemodynamic changes using a brain imaging modality, such as function near-infrared spectroscopy.

One of the advantages of fNIRS systems is their portability for real-world applications. Therefore, the online rejection of the motion artifacts in functional near-infrared spectroscopy (fNIRS) signals is one of the essential research topics. We proposed a square-root cubature Kalman filter using a weighted moving average model to tract the dynamics of motion artifacts. The rationale and feasibility of the method were discussed using simulated data generated using a Balloon model. The results show that the proposed filter has a good suppression of spike-like motion artifacts. Finally, conclusions and further study are discussed.

Background: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease, which is the sixth leading cause of death in the United State. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Currently, the combination of machine learning and neuroimaging plays a role in identifying and understanding neuropathological diseases. However, some challenges still remain, and these limitations need to be optimized for clinical MCI diagnosis. Methods: In this study, for stable identification with functional near-infrared spectroscopy (fNIRS) using the minimum resting-state time, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated based on 30 s intervals using a traditional machine learning approach and graph theory analysis. The machine learning methods were trained using temporal features of the resting-state fNIRS signal and included linear discriminant analysis (LDA), support vector machine, and K-nearest neighbor (KNN) algorithms. To enhance the diagnostic accuracy, feature representation- and classification-based transfer learning (TL) methods were used to detect MCI from the healthy controls through the input of connectivity maps with 30 and 90 s durations. Results: As in the results of the traditional machine learning and graph theory analysis, there was no significant difference among the different time windows. The accuracy of the conventional machine learning methods ranged from 55.76% (KNN, 120 s) to 67.00% (LDA, 90 s). The feature representation-based TL showed improved accuracy in both the 30 and 90 s cases (i.e., mean accuracy of 30 s: 79.37%, mean accuracy of 30 s: 74.05%). Notably, the classification-based TL method achieved the highest accuracy of 97.01% using the VGG19 pre-trained CNN model trained with the 30 s duration connectivity map. Conclusion: The results indicate that a 30 s measurement of the resting state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) may be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study&mdash, a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time&mdash, might also offer a reference for the other BCI controlled applications.

Objective. This study is to investigate a cognition-based evaluation method of commercial advertisement videos (CAV) using neuroscientific technologies. The videos include three types of duration (15, 30, and 60 sec) and two narrative structures (linear and nonlinear). In the neuro-marketing area, the brain imaging tools have become more prevalent in recent years, and it can improve product-marketing competition as a kind of novel approach. Approach. In this study, the hemodynamic responses (HRs) in the prefrontal cortex using functional near-infrared spectroscopy were measured from 8 participants while exposing them to six different types of videos. In examining the HRs, the paired sample t-test and t-value were utilized. The oxyhemoglobin (HbO) responses of three durations and two structures showed that the HRs between different structures and durations were distinguishable. Main results. For the same duration CAVs, the averaged HbO concentration level of linear structure was higher than that of nonlinear structure. The HRs of 30 s and 15 s CAVs were the higher than that of 60 s CAVS of linear and nonlinear structures. Significance. This finding suggests that the brain cortex HRs involve during the evaluation of CAVs that can be related to the structure and duration of the video. A cognition-based CAV assessment can be utilized in the digital advertisement industry. Conclusively, to persuade the consumers, a 30 s video is recommended if the structure is linear, and a 15 s video is suggested if the structure is nonlinear. Therefore, considering the production budget in advance, a wise decision can be made in developing consumer market strategies.

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer’s disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at thirteen specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46%, 87.80%, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20 s ~ 60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the non-invasive brain-computer interface (BCI). Quickly obtaining precise brain signals is very crucial for successful BCIs. This paper investigates a real-time filtering technique to remove motion artifact (MA) and low-frequency drift in the fNIRS signals. Optical intensities of two wavelengths are generated using a balloon model in the literature and an experimental paradigm. Two types of MAs (spike-like and step-like) and low-frequency drifts are generated and added to the simulated optical intensities of two wavelengths. A new dual-stage median filter (DSMF) is proposed to recover the uncontaminated signals. Five evaluation metrics are used to determine the best window sizes of the dual filters: 4 s and 9 s for the first and 18 s for the second median filter. The proposed method is compared with a wavelet-based MA correction method and spline interpolation method using the same metrics. The results show that the proposed method outperforms the compared methods in attenuating MAs and signal distortion. Finally, the designed DSMF is applied to experimental data from eight healthy subjects, in which MAs were introduced by asking the subjects to shake their heads. The filtered data of the proposed method demonstrates clean signals with no MAs and low-frequency drifts.

With growing age, the cognitive ability degrades gradually as an aging factor. For a portion of people, the cognitive capability diminishes to a great extent, which will eventually result in Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered as an intermediate stage of AD. Diagnosis of AD patients at an early stage can reduce the chance of developing into a severe condition for cognition. This study aims to investigate the MCI assessment methods (statistical analysis and individual classification) for distinguishing the healthy control (HC) and MCI patients via functional near-infrared spectroscopy (fNIRS). This study evaluated ten digital biomarkers from three brain regions and three mental tasks ( $N$ -back, Stroop, and verbal fluency task). Among these three tasks, the $N$ -back task achieved the best accuracy (76.67 %) with biomarker 2 (HbO mean from 5 to 25 sec) and 7 (HbO slope from 0 to peak value) in the middle prefrontal cortex by linear discriminant analysis (LDA). Additionally, the statistical analysis results also indicated that a significant difference ( $p$ -value $p$ -value < 0.05. It reveals that statistical analysis technique still should be improved for diagnosing MCI individuals. Machine learning (LDA) can contribute as a tool by early prediction of AD via analyzing digital biomarkers using a non-invasive technique.

This paper aims to develop a Brain-computer interface (BCI) speller utilizing eyes-closed and double-blinking electroencephalogram (EEG) signals and based on asynchronous mechanism. The proposed system comprises a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected “eyes-closed”(EC) event induces the “select”command, whereas a “double-blinking”(DB) event functions as the “undo”command. A three-class support vector machine classifier involving EEG signal analysis of three groups of events (“eyes-open” - idle state, EC, and DB) is proposed. The results show that the proposed BCI can achieve an overall accuracy of 93.8% for multi-class classification. The proposed speller is then employed in the online experiment of spelling the word “bcispeller”using a 1 s time window. Consequently, it achieves an average accuracy of 92.3% and an average spelling rate of 5 letters/min. Overall, this paper shows improvement with high accuracy and spelling rate demonstrating the feasibility and reliability of implementing a real-world BCI speller.

Owing to the advancements in the field of brain technology, numerous researches have been done to explore the prospects of application of brain signals in numerous ways. This work presents a novel approach to control a quadcopter using brain activity for eye blinking signals. EEG signals generated as a result of eye blinking are transformed into useful input to the quadcopter to perform respective commands. The work covers the execution of 8 commands using a 4-sec time window via 2 EEG electrodes. Each task to be executed in a 1-sec window. For more fast and easier control, an additional interface is provided to make the commands handier and more useful for the subject to choose between the commands. Classification accuracy shows promising results which can ensure better control for other devices like a quadcopter. Thus, this work provides a user-friendly approach for control of a quadcopter via eye blinking signals using a low cost and non-invasive technique using a wearable EEG headset.

Finite element method (FEM) was used to investigate the effect of elastic modulus and thickness of the interlayer on the bond strength for ceramic coating-substrate system in contact load condition. The maximum shear stress was acted as the criterion of the bonded strength. Firstly, the FEM simulation results indicate that the sharply changed shear stress (SCSS) can be more obviously decreased by the introduction of a interlayer as compared to by increasing the thickness of coating. Secondly, with the increase in the elastic modulus of interlayer, the SCSS of substrate/interlayer increases, while the SCSS of interlayer/coating decreases. Thirdly, with the increase in the thickness of the interlayer, the SCSS of interlayer/substrate decreases by decreasing rate. The SCSS of coating/interlayer shows the similar trends. Furthermore when the thickness increases to 0.6 mm or above, the SCSS of coating/interlayer achieve a platform value and the SCSS of interlayer/substrate do not further decreases. And then, the position of maximum shear stress is moved away from interlayer/substrate to coating/substrate and the maximum shear stress of the interlayer decreases with the increase of thickness. Finally, in present work, the optimal elastic modulus ( 1 × 10 12 Pa ) and thickness (0.6 mm) of interlayer is summarized.