Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

Action Experience, More than Observation, Influences Mu Rhythm Desynchronization

Since the discovery of mirror neurons in premotor and parietal areas of the macaque monkey, the idea that action and perception may share the same neural code has been of central interest in social, developmental, and cognitive neurosciences. A fundamental question concerns how a putative human mirror neuron system may be tuned to the motor experiences of the individual. The current study tested the hypothesis that prior motor experience modulated the sensorimotor mu and beta rhythms. Specifically, we hypothesized that these sensorimotor rhythms would be more desynchronized after active motor experience compared to passive observation experience. To test our hypothesis, we collected EEG from adult participants during the observation of a relatively novel action: an experimenter used a claw-like tool to pick up a toy. Prior to EEG collection, we trained one group of adults to perform this action with the tool (performers). A second group comprised trained video coders, who only had experience observing the action (observers). Both the performers and the observers had no prior motor and visual experience with the action. A third group of novices was also tested. Performers exhibited the greatest mu rhythm desynchronization in the 8–13 Hz band, particularly in the right hemisphere compared to observers and novices. This study is the first to contrast active tool-use experience and observation experience in the mu rhythm and to show modulation with relatively shorter amounts of experience than prior mirror neuron expertise studies. These findings are discussed with respect to its broader implication as a neural signature for a mechanism of early social learning

Emotional states detection approaches based on physiological signals for healthcare applications: A review

Mood disorders, anxiety, depression, and stress affect people’s quality of life and increase the vulnerability to diseases and infections. Depression, e.g., can carry undesirable consequences such as death. Hence, emotional states detection approaches using wearable technology are gaining interest in the last few years. Emerging wearable devices allow monitoring different physiological signals in order to extract useful information about people’s health status and provide feedback about their health condition. Wearable applications include e.g., patient monitoring, stress detection, fitness monitoring, wellness monitoring, and assisted living for elderly people, to name a few. This increased interests in wearable applications have allowed the development of new approaches to assist people in everyday activities and emergencies that can be incorporated into the smart city concept. Accurate emotional state detection approaches will allow an effective assistance, thus improving people’s quality of life and well-being. With these issues in mind, this chapter discusses existing emotional states’ approaches using machine and/or deep learning techniques, the most commonly used physiological signals in these approaches, existing physiological databases for emotion recognition, and highlights challenges and future research directions in this field. © Springer Nature Switzerland AG 2020