Zongtan Zhou

Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.

Rapid and brief communication: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition

This paper proposes a novel algorithm for image feature extraction, namely, the two-dimensional locality preserving projections (2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experimental results on the PolyU palmprint database show the effectiveness of the proposed algorithm.

Discussion: Comment on: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition

This paper proposes a novel algorithm for image feature extraction, namely, the two-dimensional locality preserving projections (2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experimental results on the PolyU palmprint database show the effectiveness of the proposed algorithm.

A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm

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.

Scene classification using a multi-resolution bag-of-features model

This paper presents a simple but effective scene classification approach based on the incorporation of a multi-resolution representation into a bag-of-features model. In the proposed approach, we construct multiple resolution images and extract local features from all the resolution images with dense regions. We then quantize these extracted features into a visual codebook using the k-means clustering method. To incorporate spatial information, two modalities of horizontal and vertical partitions are adopted to partition all resolution images into sub-regions with different scales. Each sub-region is then represented as a histogram of codeword occurrences by mapping the local features to the codebook. The proposed approach is evaluated over five commonly used data sets including indoor scenes, outdoor scenes, and sports events. The experimental results show that the proposed approach performs competitively against previous methods across all data sets. Furthermore, for the 8 scenes, 13 scenes, 67 indoor scenes, and 8 sport events data sets, the proposed approach outperforms state-of-the-art methods.

Unified SPM-ICA for fMRI analysis

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.

A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP

This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.

Gaze independent brain–computer speller with covert visual search tasks

OBJECTIVE Brain-computer interface (BCI) provides a mean of communication for the patients that are severely disabled by neuromuscular diseases. The performance of the classical P300 speller, however, declines noticeably in the gaze fixation condition. The speller paradigm presented in this paper aims to release the gaze dependency at the cost of an extra covert visual search task. METHODS Clusters of characters were presented sequentially in the near-central vision as stimulation. Participants fixed their gaze on the center, searched and recognized the target character with covert shift of attention. Random position (RP) and fixed position (FP) presentation modes designed with different searching set size (6 for RP, and ≤ 2 for FP) were examined. RESULTS Online sessions using 10 stimulus sequences achieved character accuracies of 94.4% and 96.3% for RP and FP mode, respectively. For offline overall evaluation, the peak written symbol rate (WSR) of 1.38 symbols/min was obtained, with corresponding accuracies of 87.8% (RP) and 84.1% (FP). The P300 waveform of RP mode has evident longer latency and larger amplitude. Electrooculogram (EOG) analysis indicated that the performance was independent of gaze shift. CONCLUSIONS The proposed speller could be operated effectively and gaze independently by healthy participants. SIGNIFICANCE The proposed gaze independent BCI approach promises reasonable communication capability for the profoundly paralyzed patients with head or ocular motor impairments.

Letters: An alternative formulation of kernel LPP with application to image recognition

Locality preserving projections (LPP) is a new subspace feature extraction method which seeks to preserve the local structure and intrinsic geometry of the data space. As the LPP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locality preserving projections (KLPP). Our algorithm consists of two steps: kernel principal component analysis (KPCA) plus LPP. We provide an outline for implementing KLPP. Experiments on the ORL face database and PolyU palmprint database demonstrate the effectiveness of the proposed algorithm.

A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.