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Abstract A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.

Abstract Background The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.

Type 1 diabetes mellitus (T1DM) can result in complications such as retinopathy, nephropathy, and peripheral neuropathy, which can lead to brain dysfunction. In this study, we investigated the effects of T1DM on cerebral neurovascular function in mice. Streptozotocin (STZ) is known to induce T1DM in animals; thus, we used an STZ-induced diabetes model to evaluate the effects of hyperglycemia on brain morphology and neurovascular tissue. Neurovascular coupling is the connection between neuronal activity and cerebral blood flow that maintains brain function. The ECoG-LSCI technique combines electrocorticography (ECoG) and laser speckle contrast imaging (LSCI) to detect cortical spreading depression (CSD) as a marker of neurovascular coupling and measure corresponding neurovascular function. Our results suggested that in the STZ group, hyperglycemia affected excitatory neurotransmission and metabolism, leading to reductions in intercellular signaling, somatosensory evoked potential (SSEP) amplitudes, and CSD transmission rates. Western blot data further revealed that brain-derived neurotrophic factor (BDNF) and neuronal nuclear antigen levels were reduced in the STZ group. Abnormalities in glucose metabolism in the brain and increased phosphorylation of AKT and GSK3 are hypothesized to be responsible for these decreases. Overall, this study highlights the importance of glucose metabolism in normal brain physiology and demonstrates that hyperglycemia disrupts neurovascular coupling and affects cerebral neurovascular function and that the degree of CSD is positively correlated with the extent of brain tissue damage. Further research is essential to gain a complete understanding of the related mechanisms and the implications of these findings. [ABSTRACT FROM AUTHOR]

Wound monitoring is crucial for effective healing, as nonhealing wounds can lead to tissue ulceration and necrosis. Evaluating wound recovery involves observing changes in angiogenesis. Laser speckle contrast imaging (LSCI) is vital for wound assessment due to its rapid imaging, high resolution, wide coverage, and noncontact properties. When using LSCI equipment, regions of interest (ROIs) must be delineated in lesion areas in images for quantitative analysis. However, patients with serious wounds cannot maintain constant postures because the affected areas are often associated with discomfort and pain. This leads to deviations between the drawn ROI and actual wound position when using LSCI for wound assessment, affecting the reliability of relevant assessments. To address these issues, we used the channel and spatial reliability tracker object tracking algorithm to develop an automatic ROI tracking function for LSCI systems. This algorithm is used to track and correct artificial movements in blood flow images, address the ROI position offset caused by the movement of the affected body part, increase the blood flow analysis accuracy, and improve the clinical applicability of LSCI systems. ROI tracking experiments were performed by simulating wounds, and the results showed that the intraclass correlation coefficient (ICC) ranged from 0.134 to 0.976. Furthermore, the object within the ROI affected tracking performance. Clinical assessments across wound types showed ICCs ranging from 0.798 to 0.917 for acute wounds and 0.628–0.849 for chronic wounds. We also discuss factors affecting tracking performance and propose strategies to enhance implementation effectiveness. [ABSTRACT FROM AUTHOR]

Although radiotherapy is one of the most important curative treatments for cancer, its clinical application is associated with undesired therapeutic effects on normal or healthy tissues. The use of targeted agents that can simultaneously achieve therapeutic and imaging functions could constitute a potential solution. Herein, we developed 2-deoxy-d-glucose (2DG)-labeled poly(ethylene glycol) (PEG) gold nanodots (2DG-PEG-AuD) as a tumor-targeted computed tomography (CT) contrast agent and radiosensitizer. The key advantages of the design are its biocompatibility and targeted AuD with excellent sensitivity in tumor detection via avid glucose metabolism. As a consequence, CT imaging with enhanced sensitivity and remarkable radiotherapeutic efficacy could be attained. Our synthesized AuD displayed linear enhancement of CT contrast as a function of its concentration. In addition, 2DG-PEG-AuD successfully demonstrated significant augmentation of CT contrast in both in vitro cell studies and in vivo tumor-bearing mouse models. In tumor-bearing mice, 2DG-PEG-AuD showed excellent radiosensitizing functions after intravenous injection. Results from this work indicate that 2DG-PEG-AuD could greatly potentiate theranostic capabilities by providing high-resolution anatomical and functional images in a single CT scan and therapeutic capability. [ABSTRACT FROM AUTHOR]