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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.

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.

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.

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.

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.