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ABSTRACT: Objective. Although extensive studies have shown improvement in spelling accuracy, the conventional P300 speller often exhibits errors, which occur in almost the same row or column relative to the target. To address this issue, we propose a novel hybrid brain-computer interface (BCI) approach by incorporating the steady-state visual evoked potential (SSVEP) into the conventional P300 paradigm. Approach. We designed a periodic stimuli mechanism and superimposed it onto the P300 stimuli to increase the difference between the symbols in the same row or column. Furthermore, we integrated the random flashings and periodic flickers to simultaneously evoke the P300 and SSVEP, respectively. Finally, we developed a hybrid detection mechanism based on the P300 and SSVEP in which the target symbols are detected by the fusion of three-dimensional, time-frequency features. Main results. The results obtained from 12 healthy subjects show that an online classification accuracy of 93.85% and information transfer rate of 56.44 bit/min were achieved using the proposed BCI speller in only a single trial. Specifically, 5 of the 12 subjects exhibited an information transfer rate of 63.56 bit/min with an accuracy of 100%. Significance. The pilot studies suggested that the proposed BCI speller could achieve a better and more stable system performance compared with the conventional P300 speller, and it is promising for achieving quick spelling in stimulus-driven BCI applications.
Journal of Neural Engineering 02/2013; 10(2):026012. · 3.84 Impact Factor
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ABSTRACT: During the development of neuroimaging, numerous analyses were performed to identify population differences, such as studies on age, gender, and diseases. Researchers first normalized the brain image and then identified features that represent key differences between groups. In these studies, the question of whether normalization (a pre-processing step widely used in neuroimaging studies) reduces the diversity of brains was largely ignored. There are a few studies that identify the differences between individuals after normalization. In the current study, we analyzed brain diversity on an individual level, both qualitatively and quantitatively. The main idea was to utilize brain images for identity authentication. First, the brain images were normalized and registered. Then, a pixel-level matching method was developed to compute the identity difference between different images for matching. Finally, by analyzing the performance of the proposed brain recognition strategy, the individual differences in brain images were evaluated. Experimental results on a 150-subject database showed that the proposed approach could achieve a 100% identification ratio, which indicated distinct differences between individuals after normalization. Thus, the results proved that after the normalization stage, brain images retain their main distinguishing information and features. Based on this result, we suggest that diversity (individual differences) should be considered when conducting group analysis, and that this approach may facilitate group pattern classification.
PLoS ONE 01/2013; 8(1):e54328. · 4.09 Impact Factor
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ABSTRACT: Most brain-computer interfaces (BCIs) are non-time-restraint systems. However, the method used to design a real-time BCI paradigm for controlling unstable devices is still a challenging problem. This paper presents a real-time feedback BCI paradigm for controlling an inverted pendulum on a cart (IPC). In this paradigm, sensorimotor rhythms (SMRs) were recorded using 15 active electrodes placed on the surface of the subject's scalp. Subsequently, common spatial pattern (CSP) was used as the basic filter to extract spatial patterns. Finally, linear discriminant analysis (LDA) was used to translate the patterns into control commands that could stabilize the simulated inverted pendulum. Offline trainings were employed to teach the subjects to execute corresponding mental tasks, such as left/right hand motor imagery. Five subjects could successfully balance the online inverted pendulum for more than 35s. The results demonstrated that BCIs are able to control nonlinear unstable devices. Furthermore, the demonstration and extension of real-time continuous control might be useful for the real-life application and generalization of BCI.
Neuroscience Letters 07/2012; 524(2):95-100. · 2.11 Impact Factor
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ABSTRACT: To investigate white matter volume abnormalities in patients with major depression and the effects of antidepressant treatment on white matter volume.
Magnetic resonance imaging (MRI) was performed on 32 treatment-naïve depressed patients, 17 recovered patients who had received antidepressant treatment and subsequently achieved clinical recovery and 34 matched controls.
Relative to the healthy controls, the treatment-naïve depressed patients showed increased white matter volumes in the left dorsolateral prefrontal cortex (DLPFC) and left putamen and reduced white matter volumes in the left cerebellum posterior lobe and left inferior parietal lobule. For the treatment-naïve patients, the length in months of the current depressive episode was positively correlated with the white matter volumes in both the left DLPFC and left putamen. In the recovered patients, the differences in white matter volume were no longer statistically significant relative to healthy controls. No significant difference was found in the total white matter volume among the three groups.
This study demonstrates that there were alterations in the white matter volumes of depressed patients, which might disrupt the neural circuits that are involved in emotional and cognitive function and thus contribute to the pathophysiology of depression. The finding of the significant correlations between refractoriness and the white matter volumes in the left DLPFC and left putamen combined with the finding that antidepressant treatment normalized the white matter volume of recovered patients, suggests that a quantitative, structural MRI measurement could act as a potential biomarker in depression therapy for individual subjects.
PLoS ONE 01/2012; 7(8):e44248. · 4.09 Impact Factor
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ABSTRACT: We present a novel artery-vein separation method using 0.1-Hz oscillation at two wavelengths with optical imaging of intrinsic signals (OIS). The 0.1-Hz oscillation at a green light wavelength of 546 nm exhibits greater amplitude in arteries than in veins and is primarily caused by vasomotion, whereas the 0.1-Hz oscillation at a red light wavelength of 630 nm exhibits greater amplitude in veins than in arteries and is primarily caused by changes of deoxyhemoglobin concentration. This spectral feature enables cortical arteries and veins to be segmented independently. The arteries can be segmented on the 0.1-Hz amplitude image at 546 nm using matched filters of a modified dual Gaussian model combining with a single Gaussian model. The veins are a combination of vessels segmented on both amplitude images at the two wavelengths using multiscale matched filters of single Gaussian model. Our method can separate most of the thin arteries and veins from each other, especially the thin arteries with low contrast in raw gray images. In vivo OIS experiments demonstrate the separation ability of the 0.1-Hz based segmentation method in cerebral cortex of eight rats. Two validation studies were undertaken to evaluate the performance of the method by quantifying the arterial and venous length based on a reference standard. The results indicate that our 0.1-Hz method is very effective in separating both large and thin arteries and veins regardless of vessel crossover or overlapping to great extent in comparison with previous methods.
IEEE Transactions on Medical Imaging 01/2012; · 3.64 Impact Factor
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ABSTRACT: Functional brain research has shown that the cerebral response to an external stimulus contains positive and negative signals. The positive signals are well studied, whereas explanations for the negative signals remain controversial. In this study, negative response was investigated using intrinsic optical imaging (OI) and a multi-electrode array (MEA) in rat with a hindlimb stimulus. The negative hemodynamic response (NHR) signals were measured by OI in contralateral and ipsilateral primary somatosensory forelimb, primary and secondary motor, and primary and secondary visual cortex areas. The spatial presentation of NHR signals showed diversity across subjects under an identical experimental paradigm. The NHR signals in different cortical areas had similar time courses but were in the opposite direction of the positive hemodynamic response (PHR) signals, and the amplitude of NHR signals was significantly smaller than that of PHR signals. Electrophysiological recording using an MEA in an NHR cortex area showed that spike activities decreased significantly during external stimulation, suggesting that the neuronal activity reduction has a strong relationship with the NHR signals. Our results highlight the importance of a negative response in a hemodynamics-based interpretation of neuroimaging signals.
Brain research bulletin 02/2011; 84(2):157-62. · 2.18 Impact Factor
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IEEE Trans. Med. Imaging. 01/2011; 30:2030-2043.
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ABSTRACT: In this work, we investigated the sustained negative blood oxygen level-dependent (BOLD) response (sNBR) using functional magnetic resonance imaging during a finger tapping task. We observed that the sNBR for this task was more extensive than has previously been reported. The cortical regions involved in sNBR are divided into the following three groups: frontal, somatosensory and occipital. By investigating the spatial structure, area, amplitude, and dynamics of the sNBR in comparison with those of its positive BOLD response (PBR) counterpart, we made the following observations. First, among the three groups, the somatosensory group contained the greatest number of activated voxels and the fewest deactivated voxels. In addition, the amplitude of the sNBR in this group was the smallest among the three groups. Second, the onset and peak time of the sNBR are both larger than those of the PBR, whereas the falling edge time of the sNBR is less than that of the PBR. Third, the long distance between most sNBR foci and their corresponding PBR foci makes it unlikely that they share the same blood supply artery. Fourth, the couplings between the sNBR and its PBR counterpart are distinct among different regions and thus should be investigated separately. These findings imply that the origin of most sNBR foci in the finger-tapping task is much more likely to be neuronal activity suppression rather than "blood steal."
PLoS ONE 01/2011; 6(8):e23839. · 4.09 Impact Factor
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ABSTRACT: Separating brain imaging signals by maximizing their autocorrelations is an important component of blind source separation (BSS). Canonical correlation analysis (CCA), one of leading BSS techniques, has been widely used for analyzing optical imaging (OI) and functional magnetic resonance imaging (fMRI) data. However, because of the need to reduce dimensionality and ignore spatial autocorrelation, CCA is problematic for separating temporal signal sources. To solve the problems of CCA, “straightforward image projection” (SIP) has been incorporated into temporal BSS. This novel method, termed low-dimensional canonical correlation analysis (LD-CCA), relies on the spatial and temporal autocorrelations of all genuine signals of interest. Incorporating both spatial and temporal information, here we introduce a “generalized timecourse” technique in which data are artificially reorganized prior to separation. The quantity of spatial plus temporal autocorrelations can then be defined. By maximizing temporal and spatial autocorrelations in combination, LD-CCA is able to obtain expected “real” signal sources. Generalized timecourses are low-dimensional, eliminating the need for dimension reduction. This removes the risk of discarding useful information. The new method is compared with temporal CCA and temporal independent component analysis (tICA). Comparison of simulated data showed that LD-CCA was more effective for recovering signal sources. Comparisons using real intrinsic OI and fMRI data also supported the validity of LD-CCA.
IEEE Transactions on Biomedical Engineering 09/2010; · 2.28 Impact Factor
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ABSTRACT: It is basically clear about the well known positive hemodynamic response around the activated cortexes during specific task or stimulation. However, what we know of the negative response is little. Our study concentrated on the response signals in optical imaging in rat cortex during voltage impulse stimulation in hindlimb, and we found the negative response signals out around the positive response cortex. We studied the positive and negative response signals about their spatial and temporal properties, and got some speculation about the origin of negative response signals. Our study could be the important consultation about the study on hemodynamic response signals, especially in intrinsic signals optical imaging study about brain function.
Intelligent Computation Technology and Automation, International Conference on. 05/2010; 3:482-485.
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Proceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010, July 7-9, 2010, Beijing, China; 01/2010
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Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010; 01/2010
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ABSTRACT: Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia.
NeuroImage 11/2009; 49(4):3110-21. · 5.89 Impact Factor
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ABSTRACT: Separating signal sources by maximizing the autocorrelation (also called the time delay correlation) of the interesting signal is a group of important blind source separation methods (BSS). Relying on the fact that the interesting practical sources vary more smoothly than noise, they made inspiring success in analysis of brain mapping data. However, by well investigating these algorithms, we find that they made an unnoticed assumption in maximizing the autocor- relation of signal source. Unfortunately, this assump- tion, which believes that the time delayed covariance matrix is symmetrical, is not well met in general. In the present study, a new method to maximize autocorrela- tion is proposed. It does not rely on such assumption, and shows a better performance than traditional algorithm in the compare experiment. It is also proved that, the traditional algorithms and our method are equal to each other if the assumption is met.
Measuring Technology and Mechatronics Automation, International Conference on. 04/2009; 1:403-406.
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ABSTRACT: In this paper, we proposed a noise-reduction method for functional magnetic resonance imaging (fMRI). We classified noise into structured and unstructured ones. Canonical correlation analysis was exploited to extract the underlying components among which the structured ones were recognised. Furtherly, The task-related components were detected among the structured ones by using surrogate test based on reduced autoregression model. The low degree of temporal correlation of the unstructured residuals was reduced by using randomization technique. The task-related components and the randomly permuted unstructured residuals were used to generate the reconstructed data. With application of our method, SNR of data can be significantly improved. In addition, the temporal correlation of unstructured background noise can be efficiently reduced. Twenty sets of true fMRI data for finger tapping task were processed. Some task-related areas which cannot be detected from the original data set were discerned.
IEEE Journal of Selected Topics in Signal Processing 01/2009; · 2.88 Impact Factor
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ABSTRACT: Among the available animal models of brain ischemia, it is broadly considered that cerebral cortex microvessels thrombosis induced by photochemical method is similar to human cerebral thrombosis. The brain tissues are damaged by the lack of blood and oxygen induced by thrombosis of the microvessels region. In this study, optical intrinsic signal imaging (OISI, at 546plusmn10 nm) was used to investigate the evolution of a photothrombotic lesion to somatosensory cortex. During illumination, we observed clots flow several times in illuminated region vessels, and vessels turned vague, which indicated occlusion is forming. The result of TTC staining shows that the illuminated area was infarct.
Complex Medical Engineering, 2007. CME 2007. IEEE/ICME International Conference on; 06/2007
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ABSTRACT: Ample evidence from functional magnetic resonance imaging studies has demonstrated functional brain asymmetries in the motor cortex and cerebellum within the task of finger-to-thumb (intratask), but the asymmetries across left and right hand-performed tasks of finger-to-thumb (intertask) are little known. Here, we found that intratask evoked asymmetry responses in the motor cortex and cerebellum, and interestingly in the Extra-Nuclear and insula. Intertask comparisons between left and right hand-activated images and between the asymmetries of intratasks of left and right hand, however, revealed distinct lateralization responses in the temporal cortex, extranuclear, parahippocampal gyrus, cingulate gyrus and prominently in basal ganglia. These results suggest that multiple aspects of functional asymmetries in motor organization are reflected in specific brain regions.
Neuroreport 08/2006; 17(11):1143-7. · 1.66 Impact Factor
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ABSTRACT: A combined method of univariate and multivariate analysis is presented in this paper to give a new way for fMRI data analysis. The univariate single-frame approach, which detects activations evoked by a specific task and describes the temporal characteristics of activations without prior assumptions of hemodynamic response function (HRF), can be applied as the first processing step. While the multivariate methods, i.e., spatial and temporal independent component analyses (sICA and tICA), are then brought in to analyze the derived spatiotemporal activations in the regions of interest (ROIs). The ICAs can, in the combined approach, reveal the subtle spatial patterns of the regional activation areas
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on; 11/2005
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ABSTRACT: A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.
NeuroImage 05/2005; 25(3):746-55. · 5.89 Impact Factor
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Science in China Series F: Information Sciences. 01/2005; 48:151-160.
