| dc.description.abstract | The brain and heart are physiologically related, with the brain modulate the heart rate via the autonomic nervous system. The heart transfer signals through the vagus nerve or spinal cord to the brain, allowing the brain to better modulate the heart. Physiological and psychological changes can both affect heart rate, even just through imagination. Therefore, the relationship between the brain and heart can change in different state, and the aim of this study is to investigate the changes in brain and heart activity and their correlation in different state.
Twenty participants were recruited for this study and given a shopping list to remember, then asked to select the items on the list in a virtual supermarket. Beside this task, data was collected during 5-minute resting state before and after the task. Thus, there were three stages: pre-resting, attentional(task), and post-resting state. Five-channel electroencephalography(EEG), two-channel Electrooculogram(EOG) and three-channel Electrocardiography(ECG) were recorded in all state. Pearson correlation analysis was used to assess the relationship between EEG features and heart rate variability (HRV). Two-sample t-tests and machine learning methods were used to compare the different states.
This study examined the changes in EEG and HRV features and their correlation in different states. There was significant change in EEG features during the attentional compared to resting state. During the attentional state, the sympathetic nervous system dominated. In terms of correlation, beta band in the resting state was related to heart rate feature(nn20), while all bands in the attentional state were related to heart rate feature. Theta band was related to sympathetic and parasympathetic modulation of heart (SDNN, SD2), and was present in the attentional and post-resting state. Alpha band was related to sympathetic (LF) and parasympathetic (HF) activity during resting state, modulation sympathetic activity during the pre-resting state and parasympathetic activity during the post-resting state. Machine learning results showed that brain and heart activity features in different state could be distinguished, even in different resting state. Among them, the MLP model performed the best, and the use of a fusion model achieved the highest accuracy. | en_US |