Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Music in the brain

Music is ubiquitous across human cultures — as a source of affective and pleasurable experience, moving us both physically and emotionally — and learning to play music shapes both brain structure and brain function. Music processing in the brain — namely, the perception of melody, harmony and rhythm — has traditionally been studied as an auditory phenomenon using passive listening paradigms. However, when listening to music, we actively generate predictions about what is likely to happen next. This enactive aspect has led to a more comprehensive understanding of music processing involving brain structures implicated in action, emotion and learning. Here we review the cognitive neuroscience literature of music perception. We show that music perception, action, emotion and learning all rest on the human brain’s fundamental capacity for prediction — as formulated by the predictive coding of music model. This Review elucidates how this formulation of music perception and expertise in individuals can be extended to account for the dynamics and underlying brain mechanisms of collective music making. This in turn has important implications for human creativity as evinced by music improvisation. These recent advances shed new light on what makes music meaningful from a neuroscientific perspective

Default Mode Network alterations in alexithymia: An EEG power spectra and connectivity study

Recent neuroimaging studies have shown that alexithymia is characterized by functional alterations in different brain areas [e.g., posterior cingulate cortex (PCC)], during emotional/social tasks. However, only few data are available about alexithymic cortical networking features during resting state (RS). We have investigated the modifications of electroencephalographic (EEG) power spectra and EEG functional connectivity in the default mode network (DMN) in subjects with alexithymia. Eighteen subjects with alexithymia and eighteen subjects without alexithymia matched for age and gender were enrolled. EEG was recorded during 5 min of RS. EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Compared to controls, alexithymic subjects showed a decrease of alpha power in the right PCC. In the connectivity analysis, compared to controls, alexithymic subjects showed a decrease of alpha connectivity between: (i) right anterior cingulate cortex and right PCC, (ii) right frontal lobe and right PCC, and (iii) right parietal lobe and right temporal lobe. Finally, mediation models showed that the association between alexithymia and EEG connectivity values was directed and was not mediated by psychopathology severity. Taken together, our results could reflect the neurophysiological substrate of some core features of alexithymia, such as the impairment in emotional awareness

Electroencephalographic correlates of sensorimotor integration and embodiment during the appreciation of virtual architectural environments

Nowadays there is the hope that neuroscientific findings will contribute to the improvement of building design in order to create environments which satisfy man's demands. This can be achieved through the understanding of neurophysiological correlates of architectural perception. To this aim, the electroencephalographic (EEG) signals of 12 healthy subjects were recorded during the perception of three immersive virtual reality environments (VEs). Afterwards, participants were asked to describe their experience in terms of Familiarity, Novelty, Comfort, Pleasantness, Arousal, and Presence using a rating scale from 1 to 9. These perceptual dimensions are hypothesized to influence the pattern of cerebral spectral activity, while Presence is used to assess the realism of the virtual stimulation. Hence, the collected scores were used to analyze the Power Spectral Density (PSD) of the EEG for each behavioral dimension in the theta, alpha and mu bands by means of time-frequency analysis and topographic statistical maps. Analysis of Presence resulted in the activation of the frontal-midline theta, indicating the involvement of sensorimotor integration mechanisms when subjects expressed to feel more present in the VEs. Similar patterns also characterized the experience of familiar and comfortable VEs. In addition, pleasant VEs increased the theta power across visuomotor circuits and activated the alpha band in areas devoted to visuospatial exploration and processing of categorical spatial relations. Finally, the de-synchronization of the mu rhythm described the perception of pleasant and comfortable VEs, showing the involvement of left motor areas and embodied mechanisms for environment appreciation. Overall, these results show the possibility to measure EEG correlates of architectural perception involving the cerebral circuits of sensorimotor integration, spatial navigation, and embodiment. These observations can help testing architectural hypotheses in order to design environments matching the changing needs of humans