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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.

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.

The main objective of this study is to propose a shared control strategy for improving the performance and stability of brain-computer interface (BCI)-based quadcopter movement control. With the advancement of brain imaging devices that are inexpensive, non-invasive, and portable, BCI has recently been implemented in various control applications, for example, wheelchair, telepresence robot, robotic manipulator arm, and quadcopter can be controlled using BCI-based motor imagery task or mental arithmetic task. However, using BCI modalities is still difficult to control a movement of the abovementioned machines with high stability and accuracy. Shared control is a technique that combines inputs and environment information of the system, and then, these inputs will be processed to achieve high efficient output. In this paper, the concept of the shared control will be proposed in three different methods: distance measurement sensor, eye movement detection and Global Positioning System (GPS) for avoiding an obstacle of BCI-based quadcopter control.

Nowadays a quadcopter is used in various applications, such as data collection, news broadcasting, surveying in hazardous areas, quadcopter racing competition, etc. Recently, a quadcopter movement control method has been implemented for people with disabilities by using a brain-computer interface (BCI). However, there are some limitations for BCI-based quadcopter movement control, for example, long response time in generating command signals, and unpredictable noise in human brain signals. In this paper, an image processing technique is proposed for quadcopter movement control. By detecting eye movements (i.e. forwarding gaze, left gaze, and right gaze), the image processing algorithm was implemented to distinguish the different states of eye movements and transform them into the quadcopter movement commands. This implemented technique aims to control a quadcopter without controlling by hand. Furthermore, the detection of eye movements for people who have ptosis or falling of the upper eyelid can be achieved by using the Circular Hough Transform algorithm.

The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during T seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.