Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Recent studies have shown that working memory (WM) performance can be improved by intensive and adaptive computerized training. Here, we explored the WM training effect using Electroencephalography (EEG) neurofeedback (NF) in normal young adults. In the first study, we identified the EEG features related to WM in normal young adults. The receiver operating characteristic (ROC) curve showed that the power ratio of the theta-to-alpha rhythms in the anterior-parietal region, accurately classified a high percentage of the EEG trials recorded during WM and fixation control (FC) tasks. Based on these results, a second study aimed to assess the training effects of the theta-to-alpha ratio and tested the hypothesis that upregulating the power ratio can improve working memory behavior. Our results demonstrated that these normal young adults succeeded in improving their WM performance with EEG NF, and the pre-and post-test evaluations also indicated that WM performance increase in experimental group was significantly greater than control groups. In summary, our findings provided preliminarily evidence that WM performance can be improved through learned regulation of the EEG power ratio using EEG NF.

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Object discrimination is a fundamental cognitive function for human brains. In this study, we utilized an analysis approach for single-trial electroencephalography (EEG) to analyze the spatio-temporal activation patterns of visual objects processing in human brains, and attempted to apply it to discriminate different visual objects. The spatial patterns were respectively extracted from scalp EEG and the reconstructed cortical sources, while the experiment participants were perceiving 4 different categories of visual objects (faces, buildings, cats and cars). By classifying the patterns extracted from single-trial EEG, the presented visual objects could be discriminated by a computer, and the classification accuracies may provide a quantitative index to evaluate the spatial differences of the brain activations related to different visual objects. Our results demonstrated that the spatial patterns on both the scalp and the sources levels resulted in higher classification accuracies than chance rate. We also examined the classification results using temporally non-overlapping time intervals of different event-related potential (ERP) components. The temporal changes of classification accuracies may reflect the temporal distribution of the discriminative information in the EEG responses. The present pattern extraction and classification methods may compose a computational model for single-trial EEG data analysis in investigations of the spatio-temporal activation patterns for object recognition. These spatio-temporal patterns in EEG may be useful to identifying the discriminative spatial areas and time stages to improve the accuracies and efficiencies of object discrimination. In addition, investigating and utilizing humans' cognitive mechanisms of object recognition may improve computers' processing efficiency of visual information. CitationWang C M, Hu X P, Yao L, et al. Spatio-temporal pattern analysis of single-trial EEG signals recorded during visual object recognition.

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