A Tikhonov regularization method in the inverse problem of electrical impedance tomography (EIT) often results in a smooth distribution reconstruction, with which we can barely make a clear separation between the inclusions and background. The recently popular total variation (TV)regularization method including the lagged diffusivity (LD) method can sharpen the edges, and is robust to noise in a small convergence region. Therefore, in this paper, we propose a novel regularization method combining the Tikhonov and LD regularization methods. Firstly, we clarify the implementation details of the Tikhonov, LD and combined methods in two-dimensional open EIT by performing the current injection and voltage measurement on one boundary of the imaging object. Next, we introduce a weighted parameter to the Tikhonov regularization method aiming to explore the effect of the weighted parameter on the resolution and quality of reconstruction images with the inclusion at different depths. Then, we analyze the performance of these algorithms with noisy data. Finally, we evaluate the effect of the current injection pattern on reconstruction quality and propose a modified current injection pattern.The results indicate that the combined regularization algorithm with stable convergence is able to improve the reconstruction quality with sharp contrast and more robust to noise in comparison to the Tikhonov and LD regularization methods solely. In addition, the results show that the current injection pattern with a bigger driver angle leads to a better reconstruction quality.
Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people. Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models. Significance. Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.
The stability and signal to noise ratio (SNR) of the current source circuit are the important factors contributing to enhance the accuracy and sensitivity in bioimpedance measurement system. In this paper we propose a new differential Howland topology current source and evaluate its output characters by simulation and actual measurement. The results include (1) the output current and impedance in high frequencies are stabilized after compensation methods. And the stability of output current in the differential current source circuit (DCSC) is 0.2%. (2) The output impedance of two current circuits below the frequency of 200 KHz is above 1 MΩ, and below 1 MHz the output impedance can arrive to 200 KΩ. Then in total the output impedance of the DCSC is higher than that of the Howland current source circuit (HCSC). (3) The SNR of the DCSC are 85.64 dB and 65 dB in the simulation and actual measurement with 10 KHz, which illustrates that the DCSC effectively eliminates the common mode interference. (4) The maximum load in the DCSC is twice as much as that of the HCSC. Lastly a two-dimensional phantom electrical impedance tomography is well reconstructed with the proposed HCSC. Therefore, the measured performance shows that the DCSC can significantly improve the output impedance, the stability, the maximum load, and the SNR of the measurement system.
To improve the energy utilization of magnetic field generators for biological applications, a multifunctional energy-saving magnetic field generator (ESMFG) is presented. It is capable of producing both an alternating magnetic field (AMF) and a bipolar pulse magnetic field (BPMF) with high energy-saving and energy-reuse rates. Based on a theoretical analysis of an RLC second-order circuit, the energy-saving and energy-reuse rates of both types of magnetic fields can be calculated and are found to have acceptable values. The results of an experimental study using the proposed generator show that for the BPMF, the peak current reaches 130 A and the intensity reaches 70.3 mT. For the AMF, the intensity is 11.0 mT and the RMS current is 20 A. The energy-saving and energy-reuse rates for the AMF generator are 61.3% and 63.5%, respectively, while for the BPMF generator, the energy-saving rate is 33.6%. Thus, the proposed ESMFG has excellent potential for use in biomedical applications.
The reconstruction quality in electrical impedance tomography is limited by the current injection amplitude, the injection and measurement patterns, and the measurement accuracy as well as the number and placement of electrodes. This paper dedicates to increase the number of independent voltage measurements by scanning electrode (SE), and design an optimal measurement and stimulation pattern for open electrical impedance tomography (OEIT). Firstly, several measurement patterns are, performed in OEIT, aiming to evaluate the right number of the measurement points for the imaged target in a certain depth. The results indicate that the image quality gets higher with the number of measurement point increased to some extent. Thus, it can guide the optimum design for the electrode system in OEIT. Secondly, through the numerical calculation and salt water tank experiment, in contrast to adjacent current injection pattern, cross-current-injection pattern achieves better reconstruction with higher imaging quality and penetration depth, and is more robust against data noise in deep domain. Lastly, the experiments also indicate that the electrode contact area affects the reconstruction quality and investigation depth. Therefore, OEIT with SE can improve the application in clinic, such as the detection and monitoring of vascular, breast, and pulmonary diseases.
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