Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

Emotion recognition based on affective physiological changes is a pattern recognition problem, and selecting specific physiological signals is necessary and helpful to recognize the emotions. Fingertip blood oxygen saturation (OXY), galvanic skin response (GSR) and heart rate (HR) are acquired while amusement, anger, grief and fear of 101 subjects are individually elicited by films. The affective physiological changes in multi-subject GSR, the first derivative of GSR (FD_GSR) and HR are detected by the multi-variant correlation method. The correlation analysis reveals that multi-subject HR, GSR and FD_GSR fluctuations respectively have common intra-class affective patterns. In addition to the conventional features of HR and GSR, the affective HR, GSR and FD_GSR fluctuations are quantified by the local scaling dimension and applied as the affective features. The multi-subject affective database containing 477 cases is classified by a Random Forests classifier. An overall correct rate of 74 percent for quinary classification of amusement, anger, grief, fear and the baseline state are obtained.

Emotion recognition using electroencephalogram (EEG) signals has attracted significant research attention. However, it is difficult to improve the emotional recognition effect across subjects. In response to this difficulty, in this study, multiple features were extracted for the formation of high-dimensional features. Based on the high-dimensional features, an effective method for cross-subject emotion recognition was then developed, which integrated the significance test/sequential backward selection and the support vector machine (ST-SBSSVM). The effectiveness of the ST-SBSSVM was validated on a dataset for emotion analysis using physiological signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). With respect to high-dimensional features, the ST-SBSSVM average improved the accuracy of cross-subject emotion recognition by 12.4% on the DEAP and 26.5% on the SEED when compared with common emotion recognition methods. The recognition accuracy obtained using ST-SBSSVM was as high as that obtained using sequential backward selection (SBS) on the DEAP dataset. However, on the SEED dataset, the recognition accuracy increased by ~6% using ST-SBSSVM from that using the SBS. Using the ST-SBSSVM, ~97% (DEAP) and 91% (SEED) of the program runtime was eliminated when compared with the SBS. Compared with recent similar works, the method developed in this study for emotion recognition across all subjects was found to be effective, and its accuracy was 72% (DEAP) and 89% (SEED).

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference $p\< 0.005$ and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.

Tempo is an important musical element that affects human’s emotional processes when listening to music. However, it remains unclear how tempo and training affect individuals’ emotional experience of music. To explore the neural underpinnings of the effects of tempo on music-evoked emotion, music with fast, medium, and slow tempi were collected to compare differences in emotional responses using functional magnetic resonance imaging (fMRI) of neural activity between musicians and non-musicians. Behaviorally, musicians perceived higher valence in fast music than did non-musicians. The main effects of musicians and non-musicians and tempo were significant, and a near significant interaction between group and tempo was found. In the arousal dimension, the mean score of medium-tempo music was the highest among the three kinds; in the valence dimension, the mean scores decreased in order from fast music, medium music, to slow music. Functional analyses revealed that the neural activation of musicians was stronger than those of non-musicians in the left inferior parietal lobe (IPL). A comparison of tempi showed a stronger activation from fast music than slow music in the bilateral superior temporal gyrus (STG), which provided corresponding neural evidence for the highest valence reported by participants for fast music. Medium music showed stronger activation than slow music in the right Heschl’s gyrus (HG), right middle temporal gyrus (MTG), right posterior cingulate cortex (PCC), right precuneus, right IPL, and left STG. Importantly, this study confirmed and explained the connection between music tempo and emotional experiences, and their interaction with individuals’ musical training.