Biomedical subjects

O Bai

Publications and source records attributed to O Bai.

7 recordsLinked to original sources

PubMed2001

Magnetic characteristics of new cyano-bridged two-dimensional honeycomb-like bimetallic assemblies containing Ni(II)-N[triple bond]C-Cr(III) or Ni(II)-N[triple bond]C-Cr(I) linkages.

Three cyano-bridged bimetallic assemblies, [NiL(1)](3)[Cr(CN)(6)](2).7H(2)O (1), [NiL(2)](3)[Cr(CN)(6)](2).9H(2)O (2), and [NiL(2)](3)[Cr(CN)(5)(NO)](2).9H(2)O (3) (L(1) = 3,10-dimethyl-1,3,5,8,10,12-hexaazacycloctadecane and L(2) = 3,10-diethyl-1,3,5,8,10,12-hexaazacycloctadecane), have been prepared and characterized structurally and magnetically. Complex 1 crystallizes in the monoclinic space group of C2/c with a = 25.929 A, b = 15.442(3) A, c = 19.553(4) A, beta = 106.21(3) degrees, and Z = 4, while 2 and 3 are in the trigonal space group P3 with a = b = 14.919(2) A, c = 9.5246(19) A, gamma = 120 degrees, and Z = 1 for 2 and a = b = 14.863(2) A, c = 9.3134(19) A, gamma = 120 degrees, and Z = 1 for 3. The structures of 1-3 are similar and consist of cyano-bridged two-dimensional (2D) honeycomb-like neutral Ni(3)Cr(2) layers. In each complex, [Cr(CN)(6)](3-) or [Cr(CN)(5)(NO)](3-) coordinates to three trans-[Ni(macrocycle)](2+) groups using three fac-CN(-) ligands, providing a 2D layered network. The NO group in [Cr(CN)(5)(NO)](3-) remains monodentate. Magnetic studies show the existence of a short-range ferromagnetic interaction in all of the complexes. Complexes 1 and 2 exhibit long-range antiferromagnetic ordering at 13.0 and 11.9 K, respectively, and a metamagnetic transition with critical field of ca. 1.6 kOe for 1 and 1.5 kOe for 2 at 1.8 K. Complex 3 exhibits long-range ferromagnetic ordering below 4.3 K.

Journal Article↗

PubMed2001

Accurate identification of waveform of evoked potentials by component decomposition using discrete cosine transform modeling.

This study introduces a method for accurate identification of the waveform of the evoked potentials by decomposing the component responses. The decomposition was achieved by zero-pole modeling of the evoked potentials in the discrete cosine transform (DCT) domain. It was found that the DCT coefficients of a component response in the evoked potentials could be modeled sufficiently by a second order transfer function in the DCT domain. The decomposition of the component responses was approached by using partial expansion of the estimated model for the evoked potentials, and the effectiveness of the decomposition method was evaluated both qualitatively and quantitatively. Because of the overlap of the different component responses, the proposed method enables an accurate identification of the evoked potentials, which is useful for clinical and neurophysiological investigations.

Adolescent↗

PubMed2001

Markov process amplitude EEG model for spontaneous background activity.

The Markov process amplitude (MPA) EEG model effectively representing spontaneous brain activity of the EEG was introduced. The relationship between the electrical mechanism for EEG generation and the proposed model was also investigated. The MPA EEG model was described by the sinusoidal waves with the randomly fluctuating amplitude of the first-order Markov process. The parameters of the MPA EEG model were determined optimally based on the real EEG records. The results of model outputs in the frequency domain demonstrated an excellent fit with the power spectrum of the corresponding EEG. The simulated model signal in the time domain also showed good agreement with the EEG time series. The satisfactory results from the MPA EEG model suggest its possible applicability in clinical practice. Furthermore, from the high goodness of fit, the authors think that the neurons oscillate at fixed frequencies and are modulated by synaptic interactions in accordance with the first-order Markov process.

Adult↗

PubMed2001

Parametric modeling of somatosensory evoked potentials using discrete cosine transform.

This paper introduces a parametric method for identifying the somatosensory evoked potentials (SEPs). The identification was carried out by using pole-zero modeling of the SEPs in the discrete cosine transform (DCT) domain. It was found that the DCT coefficients of a monophasic signal can be sufficiently approximated by a second-order transfer function with a conjugate pole pair. The averaged SEP signal was modeled by the sum of several second-order transfer functions with appropriate zeros and poles estimated using the least square method in the DCT domain. Results of the estimation demonstrated that the model output was in an excellent agreement with the raw SEPs both qualitatively and quantitatively. Comparing with the common autoregressive model with exogenous input modeling in the time domain, the DCT domain modeling achieves a high goodness of fitting with a very low model order. Applications of the proposed method are possible in clinical practice for feature extraction, noise cancellation and individual component decomposition of the SEPs as well as other evoked potentials.

Adolescent↗

PubMed2001

Compensation of hand movement for patients by assistant force: relationship between human hand movement and robot arm motion.

As some functional diseases in the brain, such as cerebellum dysfunction and Parkinson's disease, cause the disability related to human movement control, a compensation method was developed for improving the performance of hand movement. The compensation can be carried out by adding assistant force, which is generated from artificial equipment attached to a human arm. From the experiment of visual target tracking, the tracking trajectories recorded from both healthy persons and patients with movement disability were analyzed. It was found that the tracking trajectories were represented sufficiently by a dynamic model of a robot arm in which the differences between healthy persons and patients were characterized by the model parameters. Based on the model, it was demonstrated that the hand movement of patients could be improved by introducing an appropriate compensation. The effectiveness of the proposed compensation method was verified from a simulation study of a robot arm. The design of artificial equipment for compensating the hand movement was also presented and discussed.

Biofeedback, Psychology↗

PubMed2000

Nonlinear Markov process amplitude EEG model for nonlinear coupling interaction of spontaneous EEG.

To develop an appropriate model for representing spontaneous electroencephalography (EEG) is an important and necessary work in the field of neuroscience. The Markov process amplitude (MPA) EEG model has been proposed in our previous work for representing the features of the EEG in terms of a few parameters. However, being a linear model, the linear MPA EEG model cannot perfectly describe the spontaneous EEG that displays nonlinear phenomena. In this paper, the nonlinear Markov process amplitude (nonlinear MPA) EEG model that includes nonlinear components is introduced. The consistent consideration of the nonlinear features of the EEG investigated by N. Wiener and P. L. Nunez can be seen from the nonlinear MPA EEG model. The similarity in the time domain and the goodness of fitting in the frequency domain with respect to the ongoing EEG are shown. As a result, the EEG power spectrum can be decomposed into the spontaneous components and the nonlinearly coupled components by use of the nonlinear MPA EEG model, which is useful for a better understanding the mechanism of the EEG generation.

Biomedical Engineering↗

PubMed2000

Automatic detection of open and closed eye states in the electroencephalographic (EEG) record for background EEG interpretation by the trigger method.

Automatic detection of open and closed eye states in electroencephalographic (EEG) records was investigated in this study as a part of procedures for the precise interpretation of the background EEG to achieve a comprehensive automatic EEG interpretation system. The features of eye open and closure were extracted from the EEG using a Markov process amplitude (MPA) EEG model, which could efficiently express the features of the EEG using a small number of parameters. A new technique, named the trigger method, was also developed to overcome the difficulty resulting from large differences in the EEG features among different subjects, because an accurate detection could not be obtained by using the conventional threshold method. The proposed method gave satisfactory results which conformed with those of visual inspection by a qualified EEGer and could be clinically used as a preprocessing method for the automatic interpretation of the awake background EEG.

Adult↗