To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task-related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting-state EEG. This review aims to demystify commonly used signal processing methods for identifying task-related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task-related EEG and resting-state EEG. We then discuss the methods to extract and visualize event-related potentials in the time domain and event-related oscillatory responses in the time-frequency domain. Potential applications of advanced techniques such as source analysis and single-trial analysis are briefly discussed. We conclude this review with a short summary of task-related EEG data analysis, recommendations for further study, and caveats we should take heed of.

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non-invasively. Some important mental functions could be encoded by resting-state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting-state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting-state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft-used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Increases in depressive behaviors have been reported in patients experiencing chronic pain. In these patients, the symptoms of pain and depression commonly coexist, impairing their lives and challenging effective treatment. The hippocampus may play a role in both chronic pain and depression. A reduction in the volume of the hippocampus is related to reduced neurogenesis and neuroplasticity in cases of chronic pain and depression. Moreover, an increase of proinflammatory factors and a reduction of neurotrophic factors have been reported to modulate the hippocampal neurogenesis and neuroplasticity in chronic pain and depression. This review discusses the mechanisms underlying the depressive-like behavior accompanying chronic pain, emphasizing the structural and functional changes in the hippocampus. We also discuss the hypothesis that pro-inflammatory factors and neurotrophic factors expressed in the hippocampus may serve as a therapeutic target for comorbid chronic pain and depression.