In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.
The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.
目的 利用局部一致性（ReHo）方法探测创伤后应激障碍（PTSD）患者在静息状态下是否存在着大脑功能异常。 方法 2010年5月－7月对18例未经治疗的地震PTSD患者和19例同样经历地震但未患PTSD的对照者进行了静息态功能磁共振成像（Rs-fMRI） 扫描。应用ReHo方法处理Rs-fMRI数据，得出PTSD患者的异常脑区，并将患者存在组间差异的脑区ReHo值与临床用PTSD诊断量表（CAPS）、汉密尔顿抑郁量表（HAMD）和汉密尔顿焦虑量表（HAMA）分别进行相关分析。 结果 ① PTSD组ReHo显著增加的脑区包括右侧颞下回、楔前叶、顶下叶、中扣带回，左侧枕中回以及左/右侧后扣带回；ReHo显著降低的脑区包括左侧海马和左/右侧腹侧前扣带回。② 异常脑区中后扣带回和右侧中扣带回ReHo与HAMD呈负相关（中扣带回r=?0.575，P=0.012；右侧后扣带回：r=?0.507，P=0.032），其余脑区ReHo与临床指标无明显相关性（P＞0.05），左侧海马与CAPS的相关性相对其他脑区较大（r=?0.430，P=0.075）。 结论 PTSD患者在静息状态下即存在着局部脑功能活动的降低和增加，ReHo方法可能有助于研究PTSD患者静息状态脑活动。