This paper presents a methodology with which to construct an equivalent simulation model of closed-loop BCI testing for a vehicle component. The proposed model comprehensively takes the transfer impedance of the test configuration into account. The methodology used in this paper relies on circuit modeling and EM modeling as well. The BCI test probes are modeled as the equivalent circuits, and the frequency-dependent losses characteristics in the probe's ferrite are derived using a PSO algorithm. The measurement environments involving the harness cable, load simulator, DUT, and ground plane are designed through three-dimensional EM simulation. The developed circuit model and EM model are completely integrated in a commercial EM simulation tool, EMC Studio of EMCoS Ltd. The simulated results are validated through comparison with measurements. The simulated and measurement results are consistent in the range of 1MHz up to 400MHz.

A brain-computer interface (BCI) translates the brain activity into commands to control external devices. P300 speller based character recognition is an important kind of application system in BCI. In this paper, we propose a framework to integrate channel correlation analysis into P300 detection. This work is distinguished by two key contributions. First, a coefficient matrix is introduced and constructed for multiple channels with the elements indicating channel correlations. Agglomerative clustering is applied to group correlated channels. Second, the statistics of central tendency are used to fuse the information of correlated channels and generate virtual channels. The generated virtual channels can extend the EEG signals and lift up the signal-to-noise ratio. The correlated features from virtual channels are combined with original signals for classification and the outputs of discriminative classifier are used to determine the characters for spelling. Experimental results prove the effectiveness and efficiency of the channel correlation analysis based framework. Compared with the state-of-the-art, the recognition rate was increased by both 6% with 5 and 10 epochs by the proposed framework.

Electroencephalography (EEG) and magnetoencephalography (MEG) measure the brain signal from spatially-distributed electrodes. In order to detect event-related synchronization and desynchronization (ERS/ERD), which are utilized for brain-computer/machine interfaces (BCI/BMI), spatial filtering techniques are often used. Common spatial potential (CSP) filtering and its extensions which are the spatial filtering methods have been widely used for BCIs. CSP transforms brain signals that have a spatial and temporal index into vectors via a covariance representation. However, the variance-covariance structure is essentially different from the vector space, and not all the information can be transformed into an element of the vector structure. Grassmannian embedding methods, therefore, have been proposed to utilize the variance-covariance structure of variational patterns. In this paper, we propose a metric learning method to classify the brain signal utilizing the covariance structure. We embed the brain signal in the extended Grassmann manifold, and classify it on the manifold using the proposed metric. Due to this embedding, the pattern structure is fully utilized for the classification. We conducted an experiment using an open benchmark dataset and found that the proposed method exhibited a better performance than CSP and its extensions.

We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

The development of 3D High Efficiency Video Coding (3D-HEVC) has resulted in a growing interest in the compression of depth-maps. To achieve better intra prediction performance, the Depth Modeling Mode (DMM) technique is employed as an intra prediction technique for depth-maps. However, the complexity and computation load have dramatically increased with the application of DMM. Therefore, in view of the limited colors in depth-maps, this paper presents a novel fast intra coding scheme based on Base Colors and Index Map (BCIM) to reduce the complexity of DMM effectively. Furthermore, the index map is remapped, and the Base Colors are coded by predictive coding in BCIM to improve compression efficiency. Compared with the intra prediction coding in DMM, the experimental results illustrate that the proposed scheme provides a decrease of approximately 51.2% in the intra prediction time. Meanwhile, the BD-rate increase is only 0.83% for the virtual intermediate views generated by Depth-Image-Based Rendering.

In this work, a measurement-based procedure aimed at deriving a behavioral model of Bulk Current Injection (BCI) probes clamped onto multi-wire cable bundles is proposed. The procedure utilizes the measurement data obtained by mounting the probe onto the calibration jig for model-parameters extraction, and 2D electromagnetic simulations to adapt such parameters to the specific characteristics of the cable bundle under analysis. Outcome of the analysis is a behavioral model which can be easily implemented into the SPICE environment. Without loss of generality, the proposed model is here used to predict the radio-frequency noise stressing the terminal units of a two-wire harness. Model accuracy in predicting the common and differential mode voltages induced by BCI at the line terminals is assessed by EM modeling and simulation of the involved injection setup by the commercial software CST Microwave Studio.

We have developed a portable NIRS-based optical BCI system that features a non-invasive, facile probe attachment and does not require muscle movement to control the target devices. The system consists of a 2-channel probe, a signal-processing unit, and an infrared-emission device, which measures the blood volume change in the participant's prefrontal cortex in a real time. We use the threshold logic as a switching technology, which transmits a control signal to a target device when the electrical waveforms exceed the pre-defined threshold. Eight healthy volunteers participated in the experiments and they could change the television channel or control the movement of a toy robot with average switching times of 11.5 ± 5.3 s and the hit rate was 83.3%. These trials suggest that this system provides a novel communication aid for people with motor disabilities.

We developed a novel movement-imagery-based brain-computer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, time-frequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were classified as right or left based on β-band event-related synchronization using the cumulative distribution function of pretrigger EEG noise. To test the performance of the BCI, EEGs during the execution and imagination of right/left wrist-bending movements were measured from 63 locations over the entire scalp using eight healthy subjects. The highest classification accuracies were 84.4% and 77.8% for real movements and their imageries, respectively. The accuracy is significantly higher than that of previously reported machine-learning-based BCIs in the movement imagery task (paired t-test, p < 0.05). It has also been demonstrated that the highest accuracy was achieved even though subjects had never participated in movement imageries.

The highly accurate BCI using alpha waves was developed for controlling the robot arm, and real-time operation was succeeded by using noninvasive electrodes. The significant components of the alpha wave were identified by spectral analysis and confirmation of the amplitude of the alpha wave. When the alpha wave was observed in the subject, the subjects were instructed to select the multiple decision branches, concerning 7 motions (including "STOP") of a robot arm. As a result, high accuracy (70-95%) was obtained, and the subject succeeded in transferring a small box by controlling the robot arm. Since high accuracy was obtained by use of this method, it can be applied to control equipments such as a robot arm. Since the alpha wave can be easily generated, the BCI using alpha waves does not need more training than that using other signals. Moreover, we tried to reduce the false positive errors by effectively detecting artifacts using spectral analysis and detecting signals of 50 µV or more. As a result, the false positive errors could be reduced from 25% to 0%. Therefore, this technique shows great promise in the area of communication and the control of other external equipments, and will make great contribution in the improvement of Quality of Life (QOL) of mobility disabled.

In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

A simple method for diagnosing noise immunity of printed circuit boards (PCBs) by the bulk current injection (BCI) test was proposed, which can contribute to the PCB trace designs for common-mode noise. A grading index, which is defined as the ratio of the stray capacitances with and without critical IC of malfunction, was introduced to distinguish the PCB susceptible to the common-mode noise. This proposed method was validated experimentally using four PCBs with the same circuit but different trace design. It was observed that the noise immunity of PCBs had a good correlation with the values of these grading indices.

This paper describes the expanded generalized method of characteristics (GMC) in order to handle large linear interconnect networks. The conventional GMC is applied to modeling each of transmission lines. Therefore, this method is not suitable to deal with large linear networks containing many transmission lines. Here, we propose the expanded GMC method to overcome this problem. This method computes a characteristic impedance and a new propagation function of the large linear networks containing many transmission lines. Furthermore the wave propagation delay is removed from the new wave propagation function using delay evaluation technique. Finally, it is shown that the present method enables the efficient and accurate simulation of the transmission line networks.

In this paper, we present an efficient approach for technology scaling of MOS analog circuits by using circuit optimization techniques. Our new method is based on matching equivalent circuit parameters between a previously designed circuit and the circuit undergoing redesign. This method has been applied to a MOS operational amplifier. We were able to produce a redesigned circuit with almost the same performance in under 4 hours, making this method 5 times more efficient than conventional methods

We have studied a circuit partitioning approach in the view of parallel circuit simulation on a MIMD parallel computer. In parallel circuit simulation, a circuit is partitioned into equally sized subcircuits while minimizing the number of interconnection nodes. Besides circuit partitioning time should be short enough compared with the total simulation time. From the details of circuit simulation time, we found that balancing subcircuits is critical for low parallel processing, whereas minimizing the interconnection nodes is critical for highly parallel processing. Our circuit partitioning approach consists of four steps： Grouping transistors, initial partitioning the transistor-groups, minimizing the number of interconnection nodes, and balancing the subcircuits. It is based on an algorithmic approach, and can directly control the tradeoffs between balancing subcircuits and minimizing the interconnection nodes by adjusting the parameters. We partitioned a test circuit with 3277 transistors into 4, 9, ... , 64 subcircuits, and did parallel simulations using PARACS, our parallel circuit simulator, on an AP1000 parallel computer. The circuit partitioning time was short enough-less than 3 percent of the total simulation time. The highest performance of parallel analysis using 49 processors was 16 times that of a single processor, and that for total simulation was 9 times.