Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8–13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.

Physiological studies have shown that self-body images receive unique recognition processing in a wide range of brain areas, from the frontal lobe to the parietal-occipital cortex. Event-related potential (ERP) studies have shown that the self-referential effect on the image of a hand increases P300 components, but such studies do not evaluate brain oscillatory activity. In this study, we aimed to discover the self-specific brain electrophysiological activity in relation to hand images. ERPs on the fronto-parietal midline were elicited by a three-stimulus visual oddball task using hand images: the self-hand, another hand (most similar to the self-hand), and another hand (similar to the self-hand). We analyzed ERP waveform and brain oscillatory activity by simple averaging and time-frequency analysis. The simple averaging analysis found no significant differences between the responses for the three stimulus tasks in all time windows. However, time-frequency analysis showed that self-hand stimuli elicited high gamma ERS in 650–900 ms at the Cz electrode compared to other hand stimuli. Our results show that brain activity specific to the self-referential process to the self-hand image was reflected in the long latency gamma band activity in the mid-central region. This high gamma-band activity at the Cz electrode may be similar to the activity of the mirror neuron system, which is involved in hand motion.

Background/Objectives: This exploratory pilot study examined whether motor imagery ability is associated with electroencephalography (EEG) oscillatory activity during single-joint motor imagery. Methods: Eleven healthy young adults performed motor execution, kinesthetic motor imagery (KMI), and visual motor imagery (VMI) of right wrist dorsiflexion. Motor imagery ability was assessed using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). Time–frequency EEG data were analyzed using cluster-based permutation tests. Based on the study hypotheses, kinesthetic KVIQ scores were examined in relation to task-onset-locked KMI activity, and visual KVIQ scores were examined in relation to task-offset-locked VMI activity. A Bonferroni-adjusted threshold of p < 0.025 was applied across the two analyses. Results: No condition-level EEG differences were observed among motor execution, KMI, and VMI. Kinesthetic KVIQ scores were negatively associated with 6.5–13 Hz activity at O1 during KMI (p = 0.024), whereas visual KVIQ scores were positively associated with post-task 10–16.5 Hz activity at F7 during VMI (p = 0.023). Both met the Bonferroni-adjusted significance threshold. The corresponding descriptive Pearson correlations were r = −0.869 and r = 0.937. Conclusions: These exploratory findings should not be interpreted as confirmatory evidence of stable brain–behavior associations; the reported correlations are descriptive summaries based on clusters selected from the same dataset. Replication in a larger, independent study with prespecified analyses and objective measures of imagery performance is needed.