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Human motor imagery (MI) tasks evoke EEG signal changes. The features of these changes appear as subject-specific temporal traces of EEG rhythmic components at specific channels located over the scalp. Accurate classification of MI tasks based upon EEG may lead to a noninvasive brain-computer interface (BCI) to decode and convey intention of human subjects. We have previously proposed two novel methods on time-frequency feature extraction, expression and classification for high-density EEG recordings (Wang & He: J Neural Eng, 1: 1–7, 2004; Wang, Deng & He: Clin Neurophysiol, 115: 2744–2753, 2004). In the present study, we refined the above time-frequency- spatial approach and applied it to a one-dimensional “cursor control” BCI experiment with online feedback. Through offline analysis of the collected data, we evaluated the capability of the present refined method in comparison with the original time- frequency-spatial methods. The enhanced performance in terms of classification accuracy was found for the proposed approach, with a mean accuracy rate of 91.1% for two subjects studied.

Objective Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been studied to achieve high-resolution reconstruction of the spatiotemporal cortical current density (CCD) distribution. However, mismatches between these two imaging modalities may occur due to their different underlying mechanisms. The aim of the present study is to investigate the effects of different types of fMRI–EEG mismatches, including fMRI invisible sources, fMRI extra regions and fMRI displacement, on the fMRI-constrained cortical imaging in a computer simulation based on realistic-geometry boundary-element-method (BEM) model. Methods Two methods have been adopted to integrate the synthetic fMRI and EEG data for CCD imaging. In addition to the well-known 90% fMRI-constrained Wiener filter approach (Liu AK, Belliveau JW, Dale AM. PNAS 1998;95:8945–8950.), we propose a novel two-step algorithm (referred to as ‘Twomey algorithm’) for fMRI–EEG integration. In the first step, a ‘hard’ spatial prior derived from fMRI is imposed to solve the EEG inverse problem with a reduced source space; in the second step, the fMRI constraint is removed and the source estimate from the first step is re-entered as the initial guess of the desired solution into an EEG least squares fitting procedure with Twomey regularization. Twomey regularization is a modified Tikhonov technique that attempts to simultaneously minimize the distance between the desired solution and the initial estimate, and the residual errors of fitness to EEG data. The performance of the proposed Twomey algorithm has been evaluated both qualitatively and quantitatively along with the lead-field normalized minimum norm (WMN) and the 90% fMRI-weighted Wiener filter approach, under repeated and randomized source configurations. Point spread function (PSF) and localization error (LE) are used to measure the performance of different imaging approaches with or without a variety of fMRI–EEG mismatches. Results The results of the simulation show that the Twomey algorithm can successfully reduce the PSF of fMRI invisible sources compared to the Wiener estimation, without losing the merit of having much lower PSF of fMRI visible sources relative to the WMN solution. In addition, the existence of fMRI extra sources does not significantly affect the accuracy of the fMRI–EEG integrated CCD estimation for both the Wiener filter method and the proposed Twomey algorithm, while the Twomey algorithm may further reduce the chance of occurring spurious sources in the extra fMRI regions. The fMRI displacement away from the electrical source causes enlarged localization error in the imaging results of both the Twomey and Wiener approaches, while Twomey gives smaller LE than Wiener with the fMRI displacement ranging from 1–2 cm. With less than 2 cm fMRI displacement, the LEs for the Twomey and Wiener approaches are still smaller than in the WMN solution. Conclusions The present study suggests that the presence of fMRI invisible sources is the most problematic factor responsible for the error of fMRI–EEG integrated imaging based on the Wiener filter approach, whereas this approach is relatively robust against the fMRI extra regions and small displacement between fMRI activation and electrical current sources. While maintaining the above advantages possessed by the Wiener filter approach, the Twomey algorithm can further effectively alleviate the underestimation of fMRI invisible sources, suppress fMRI spurious sources and improve the robustness against fMRI displacement. Therefore, the Twomey algorithm is expected to improve the reliability of multimodal cortical source imaging against fMRI–EEG mismatches. Significance The proposed method promises to provide a useful alternative for multimodal neuroimaging integrating fMRI and EEG.

Feasibility of continuously and simultaneously recording visual evoked potentials (VEPs) with fMRI was assessed by quantitatively comparing cortical source images by means of receiver operating characteristic (ROC) curve analysis. The averaged EEG source images coincided well with simultaneously acquired fMRI activations. Strong correlation was found between the cortical source images of VEPs recorded inside and outside the scanner, despite slight difference in latencies and amplitudes of P1 peak existed in waveforms. Application of fMRI prior information strengthened correlation between estimated source images as well as resulted in source estimates with higher spatial resolution. The present results demonstrate that reliable cortical source images can be acquired during simultaneous fMRI scanning and they may be used for multimodal functional source imaging studies.

We have developed a novel time-frequency approach of classification of motor imagery (MI) tasks for brain-computer interface (BCI) applications. Through off-line data analysis on data collected during a "cursor control" experiment, we evaluated the capability of our proposed method in revealing the major features of the EEG control and enhancing MI classification accuracy. The pilot results in two human subjects are promising, with a mean accuracy rate of 87.9%, suggesting the feasibility of defining a new and more reliable EEG-based BCI

We have developed new algorithms for classification of motor imagery tasks for brain-computer interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space-domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvasive brain-computer interface applications

Cortical imaging technique (CIT) is an effective spatial enhancement approach to noninvasively image dynamic brain activity with high spatial resolution. The accuracy of forward problem in CIT has an importance on the accuracy of estimated cortical potentials. In this paper, we investigate finite-element-algorithms relating the cortical potentials to the scalp potentials. The numerical accuracy of the FEM algorithm being considered was assessed by computer simulations. The present simulation results suggest that the present FEM model has the similar accuracy as compared with the conventional model, but offers convenience in the application. The present algorithm has been tested in a realistic geometry head model.

The significant market growth of mobile commerce brings challenges for individuals, society and technology. The creation and maintenance of secure identities for online payment has emerged as an important priority for business and consumers. In order to address some of these challenges, an agent-oriented Biometric Mobile Payment (BMP) system is developed to provide a platform-independent payment system compatible with mobile phones. This system combines two elements: a mobile two-dimensional barcode reader and a Mobile Biometric Identification (MBI) system. In this application, mobile phone users can use the two-dimensional barcode reader to scan the barcodes of products or a bill that they would like to purchase or pay, and make payments via the mobile phone using the MBI system. By using this approach, fraud committed by impostors and the risks for financial institutions and mobile phone operators can be greatly reduced. By adopting the agent-oriented system design, the MBI system and mobile two-dimensional barcode reader can be used as stand-alone tools or they can collaborate seamlessly as a BMP system. The reusability of the software agents also allows more applications to use the MBI and BMP systems in future applications.

On etudie le mouvement des parois de domaines ferroelectriques dans un cristal TGS dans lequel la transition de phase ordre-desordre ferroelectrique du second ordre prend place a la temperature de Curie

Alkaline peroxide mechanical pulp (APMP) is a newly emerging high yield pulp (HYP) with numerous advantages. However, the drawback of the alkaline peroxide mechanical pulp from untreated plant biomass is its poor network strength. In this work, 3-chloro-2-hydroxypropyl trimethyl ammonium chloride (CHPTAC) modification was proposed to enhance pulp network strength by fiber surface modification that could enhance fiber bonding. Three factors were analyzed by response surface methodology (RSM) to optimize treatment conditions based on factorial designs. The results showed that the optimal conditions were CHPTAC dosage of 0.8% (oven-dry pulp), NaOH dosage of 0.1% (oven-dry pulp), and pulp concentration of 8%. The modified pulp fibers were characterized by elemental analysis, charge density analysis, Fourier transform infrared spectroscopy (FTIR), thermal gravity analysis (TGA), and internal bond strength analysis, as well as zero span tensile analysis. The physical strength of the modified APMP pulp was increased in terms of tensile index, tear index, and burst index. After modification, the tensile index, tear index, and burst index increased by 35.3%, 29.2%, and 16.7% respectively. The internal bonding strength increased by 144.4%; however, the increase of zero span tensile index of modified pulp fibers was insignificant.

In the present study, a heart-model-based imaging approach was applied to the rabbit heart, and 3-D cardiac activation sequences were estimated noninvasively from body surface potential maps during in vivo ventricular pacing from different sites. The estimated activation sequences exhibited earliest sites of cardiac activation and patterns of activation that were consistent with those constructed from 3-D activation sequences obtained from simultaneously recorded bipolar electrogram data (from nearly 160 intramural recording sites). The relative errors between the imaged and measured activation sequences were 0.291/spl plusmn/0.009 for the cases studied. The present study suggests that the heart-model-based imaging approach represents an important alternative for noninvasive imaging of cardiac activation that could be useful for basic cardiovascular research and for the diagnosis and management of cardiac diseases.