Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.
This study examined the cortical control of gait in healthy humans using functional magnetic resonance imaging (fMRI). Two block-designed fMRI sessions were conducted during motor imagery of a locomotor-related task. Subjects watched a video clip that showed an actor standing and walking in an egocentric perspective. In a control session, additional fMRI images were collected when participants observed a video clip of the clutch movement of a right hand. In keeping with previous studies using SPECT and NIRS, we detected activation in many motor-related areas including supplementary motor area, bilateral precentral gyrus, left dorsal premotor cortex, and cingulate motor area. Smaller additional activations were observed in the bilateral precuneus, left thalamus, and part of right putamen. Based on these findings, we propose a novel paradigm to study the cortical control of gait in healthy humans using fMRI. Specifically, the task used in this study--involving both mirror neurons and mental imagery--provides a new feasible model to be used in functional neuroimaging studies in this area of research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.