Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2024-04-29
Abstract： The limited training samples pose a significant obstacle to the practical application of brain-computer interface based on error-related potentials (ErrPs), affecting their recognition accuracy. To enhance ErrP recognition under such constraints, we propose a transfer discriminant subspace analysis (TDSA) algorithm that leverages a data alignment strategy. This algorithm extracts a shared discriminant subspace from electroencephalogram samples of both source and target subjects, capturing inter-class differences. By applying temporal alignment within this subspace, it effectively reinforces common features across subjects. We evaluate six different data alignment transfer learning strategies using two publicly available datasets. The results demonstrate that the TDSA algorithm achieves a 6.07% improvement in balanced accuracy for dataset 1 compared to the suboptimal Euclidean alignment method and a 7.88% improvement over non-transfer learning methods. Remarkably, with only 60~100 target subject data samples for training, the TDSA algorithm approaches the classification performance of traditional strategies that require 210~350 samples. This provides a new perspective for facilitating the data alignment of ErrPs.
Peer Review Status:
Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2024-03-20
Abstract： Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by high prevalence, heterogeneity, and lifelong impact. The underlying mechanisms of ASD remain largely unknown. Multimodal magnetic resonance imaging (MRI) has emerged as a novel tool to unveil the neuroimaging mechanisms of ASD. Studies based on single-modal MRI have already revealed widespread abnormalities in brain structure, function, and network connectivity in individuals with ASD. The affected regions encompass the amygdala, fusiform gyrus, orbitofrontal cortex, medial prefrontal cortex, anterior cingulate cortex, superior temporal sulcus, and insula, many of which are implicated in the social brain network. While frameworks for multimodal brain imaging analysis, involving image-level fusion, feature-level fusion, and decision-level fusion, offer multidimensional and multilevel information for understanding neural mechanisms in participants, research on ASD based on multimodal MRI fusion is still in its early stages. Moreover, ASD-assisted diagnosis and subtype classification models based on MRI features hold promise for providing objective evidence for clinical diagnosis and treatment. Future research should aim to construct an integrated analysis framework that fuses multimodal brain imaging, incorporating information from various dimensions such as brain function, structure, and networks. This approach will comprehensively delineate the developmental patterns of ASD and reveal its atypical neurodevelopmental mechanisms. Additionally, future studies need to delve into the abnormal mechanisms of the social brain network in ASD, explore social impairment circuits, and identify potential precision neural regulatory targets, thereby assisting clinical efforts in achieving precise diagnosis and treatment for ASD.
Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2023-05-27
Wang, Dongping
Jin, Kai
Hu, Chenxuan
Du, Maohua
Belgaid, Yacine
Shi, Subao
Li, Jiahao
Hu, Siyi
Yu, Jun
Nathan, Arokia
Ma, Hanbin
Abstract： Digital liquid sample handling is an enabling tool for cutting-edge lifesciences research. Similar to Field Programmable Gate Arrays (FPGA) in Integrated Circuits, we present here an active-matrix thin-film electronicsbased digital microfluidics system, which will be henceforth referred to as Field Programmable Droplets Arrays (FPDA). The system contains 256 × 256 pixels in an active area of 10.65 cm2, which is capable of manipulating thousands of individually addressable liquid droplets simultaneously. By leveraging a customised TFT-based circuit design solution, it becomes possible to programmatically manipulate droplets at the pixel level. The minimum achievable droplet volume is around 0.5 nl, which is two orders of magnitude smaller than the state-of-the-art reported1. The movement of droplets can be either pre-programmed or controlled in real-time. The FPDA system shows great potential of the ubiquitous thin-film electronics technology in digital liquid handling. These efforts will make it possible to create a true programmable lab-on-a-chip device to enable great advances in life science research.
Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2022-04-17
Abstract：
Thin-film semiconductor devices as switching elements are perfect fit for electrodes-array based digital microfluidics. With support of large-area electronics technology, high-resolution digital droplets (diameter around 100 μm) contains single cell can be generated by pre-programmable addressing signals. Single-cell generation and manipulation is a foundation of single-cell research, which demands ease of operation, multifunctional and accurate tools. Herein, we report an active-matrix digital microfluidic platform for single-cell generation and manipulation enabled by large-area electronics technology. The active device contains 26,368 electrodes that can be independently addressed to perform parallel and simultaneous droplets and even single cells manipulation. An on-chip generated single droplet volume limit of 500 pL has been reported, proving the continuous and stable movement of the droplet containing cells for over 1 hour. Furthermore, the success rate of single droplet formation can be higher than 98% and able to generate around 10 single cells within 10 seconds. A pristine single cell generation rate of 29% is achieved without any further sorting process
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Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2020-04-05
Abstract： Electrochemical impedance biosensors measure the impedance varies of the solution during the biochemical process, so it can realize label-free biological detection. However, the biological signal to be detected is very weak, thus requiring signal amplification circuit. Printed organic thin film transistor (OTFT) has many advantages such as low cost, flexible bending, biocompatibility, which is suitable for biological detection. This work built up amplification circuit for electrochemical impedance test based on OTFT, different concentrations of phosphate buffer solution (PBS) used as test samples were measured with the above circuit. The results show that OTFT-based circuit has a good implementation of signal amplification, which lay a foundation for the application of printed OTFT in the electrochemical impedance biosensors.
Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2018-03-15
Abstract： Limited to the system architecture in conventional CT, Artifacts induced by respiratory motion during routine diagnosis severely degrades the image quality. A novel micro-CT system performing well in a quasi-static way is proposed according to the principle of field emission. It employs 35 carbon nanotubes based x-ray tubes and 5 flat detectors, motion artifacts can be greatly suppressed by reducing the gantry rotation times and range under the control of the external sequential when compared with conventional CT system. To validate the feasibility of the proposed system, iterative reconstruction algorithm is adopted on simulation due to sparse sampling .
Peer Review Status:Awaiting Review
Subjects: Biology >> Neurobiology Subjects: Psychology >> Cognitive Psychology Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2018-03-15
Abstract： Hypnosis is an effective psychological technology in respiratory motion control. In this study, functional magnetic imaging was applied to an intra-subject (n＝13) design hypnosis experiment guided by hypnotists to analyze the respiratory motion control and neural activity in hypnosis. As a result, increased brain activities were observed in visual cortex, sensorimotor cortex, posterior cingulate cortex and middle temporal gyrus, and decreased in dorsolateral prefrontal cortex, cerebellum posterior lobe and supramarginal gyrus. Moreover, compared with normal state, enhanced correlation of brain activities (normal state, r＝0.64; hypnosis state, r＝0.80) was observed within large-scale resting-state networks. Increased connectivity between sensorimotor cortex and visual cortex in hypnosis was also observed, which implies their critical roles in neural mechanisms of hypnosis for respiration control and involvement of cognitive and perceptual processing therein. This study provides new insights for hypnosis study in psychology and cognitive neuroscience.
Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2018-03-14
Abstract： X-ray shading artifacts lead to CT number inaccuracy, image contrast loss and spatial non-uniformity, and therefore are considered as one of the fundamental limitations of CBCT. In order to solve this problem, a novel shading correction method was proposed. Specifically, we first use multi-threshold segmentation algorithm to segment the original CT image for constructing a template image where each structure is filled with the same CT number of a specific tissue type. Then, the L0 norm smoothing algorithm is used to smooth the CBCT image for constructing an image without texture. By subtracting the template from the image without texture, the residual images from various error sources are low-pass filtered to generate the estimated shading artifacts. Finally, the estimated shading artifacts are added back to the original image for shading correction. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 113 HU to be less than 13 HU and decreases the non-uniformity from over 9% to be less than 1%. The experimental results demonstrate that the proposed shading correction method using L0 norm smoothing and image segmentation can effectively correct the shading artifacts and its feasibility in clinical application is validated.
Peer Review Status:Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering Subjects: Library Science，Information Science >> Information Science submitted time 2018-03-14
Abstract： Objective Wearable blood pressure measurement technology is of significant vale in preventing and diagnosing of hypertension. Patent analysis on this area is of significant vale for patent layout. Methods Global patents on wearable blood pressure measurement are analyzed with patent analysis tools, such as Thomson data analyzer (TDA).Methods such as statistics approach, clustering method, citation analysis, similarity matrix are used to reveal the overall development of wearable blood pressure measurement technology in the world. Result Cuffless blood pressure measurement is found to be an important technical branch of wearable blood pressure measurement through statistical analysis and similarity matrix. The core patent in this area is owned by American companies such as Sotera. The research and technology transformation in China is inadequate in this area. Some advices were proposed on research and development in this areas.
Peer Review Status:Awaiting Review
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