To examine the EEG characteristics of the elderly people who participated in the medical checkup for dementia. Subjects were 402 elderly people in population-based dementia prevalence survey (2010) of Tone-Town, Ibaraki. EEG was recorded for 5 minutes in sitting position. EEG of 318 elderly people (166 males [age 77.5±7.8], 152 females [age 76.3±7.7]) were readable for interpretation. EEG abnormalities rate were 5.7% in Normal Control (NLC), 9.6% in Within Normal Limits (WNL), 18.5% in Mild Cognitive Impairment (MCI) and 42.9% in Alzheimer’s Disease (AD). Rate of less-than 9 Hz background activities were significantly low on NLC and significantly high on AD. The appearance of elderly-specific EEG finding was: Kappa rhythms 69.8%, Wicket spikes 3.5% and temporal slow waves of the elderly (TSE) 15.7%. The more the kappa rhythm appeared, the older the patients were. The frequency of background activities were related to the morbidity of dementia. Kappa rhythms may play a certain additional role in the age-related change within a brain.

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This study aimed to determine the influence of motor imagery of finger opposition movements with differing complexities on the excitability of spinal neural function using F-wave analysis. Fifteen healthy subjects were enrolled in this study. F-waves were recorded under the following 4 conditions: at rest; during motor imagery of opposition movement of the right thumb and index finger (task 1); during motor imagery of opposition movement of the right thumb and index, middle, ring, and little fingers (task 2); and during motor imagery of opposition movement of the right thumb and index, ring, middle, and little fingers (task 3). To determine whether the subjects could recollect each task, a questionnaire was distributed after the experiment. The F/M amplitude ratio was significantly higher during task 2 than at rest. F-wave persistence was significantly higher during tasks 1 and 2 than at rest. The questionnaire score was significantly lower for task 3 than for tasks 2 and 1. These results suggest that the excitability of spinal neural function increases during motor imagery of finger opposition movements with differing complexities.

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Under the noisy environment, music was presented through either of two headphones with different sound insulation properties. Pulse wave on index finger was measured during experiment and stress response due to music with contaminated noise was examined using heart rate variability. Twenty healthy adults (ten men and ten women, average age 23.2±3.6 years) had participated in this experiment. Music (concerto “four seasons” by Vivaldi) was reproduced from CD and presented for 40 sec in block design to the subject through either of headphones with an interruption of 40 sec without music. Three blocks were done in this experiment.
According to the subjective reports after the experiment, audibility of music depends upon an insulation property of a headphone. Furthermore, variability of heart rate significantly altered due to music presentation under the noisy environment, i.e., values of L/T and of CV-RR decreased significantly under the condition of insufficient sound insulation. I confirmed that autonomic nervous activity might be also affected significantly due to music presentation through headphone with insufficient sound insulation under the noisy environment.

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Many guidelines for digital EEG (dEEG) have been published in the current dEEG era since 1990s in which they have described and defined its mechanical and software-based functions. However, little has been done for practical guidelines to record, read and interpret dEEG so far exclusively from the viewpoint of EEG technologists and EEGers. Therefore, this guideline aims to provide us with the information how to use dEEG most effectively by knowing 1) how to record and manipulate dEEG to maximize its function, and 2) how to read dEEG to maximize its function and to minimize the interpretation error and the spent time.
It is very important for EEG technologists to record dEEG with good quality. They should display and monitor dEEG in the most suitable condition, being apart from the fixed recording condition. Namely, they should change any display conditions including montage, filter setting, time window, and amplitude depending on the patients’ condition, as done previously in the analogue EEG era. Thus they could inspect and record the best dEEG while recording. As a result, (1) it provides EEGers with the most suitable display condition to read EEG immediately after recording, and (2) it gives us the suitable dEEG for further data processing such as frequency analysis, power analysis, topographical mapping, time-frequency analysis, source estimation, etc.
For EEG reading, although EEGers could anecdotally choose any display conditions (i.e., montage, filter setting, time window, and amplitude), it is usually the most effective way to read EEG as it was inspected and displayed while recording by the EEG technologists as long as dEEG was well inspectionally displayed. When needed, they could change display conditions any time. However, it should be avoided that they arbitrarily select only one or two montages to complete dEEG reading. They should read dEEG by means of at least two montages or more in combination to complement the features of each montage as so-called rational montage selection, e.g., referential montage with ipsilateral earlobe reference easily causes earlobe activation (reference activation) in the temporal abnormality and thus it is carefully interpreted or not selected in patients with temporal abnormality. Prior to final interpretation, they should make sure that they selected appropriate montages based on the clinical information, e.g., averaged reference is rather recommended to extract and localize the maximum active area properly unless the interested EEG activity is generalized, and it is not applied to read generalized activity.
While reading, EEGers could effectively use the tools of data processing such as frequency analysis, power analysis, topographical mapping, time-frequency analysis, source estimation, etc. Thus it helps them inspect dEEG visually with more accuracy, and increases both sensitivity and specificity to detect the abnormal finding.
Finally, this guideline could provide any trainees with practical learning way to read dEEG most effectively because dEEG is the useful, self-learning tool how to read EEG. Teaching files of dEEG with EEG reports enormously enhance this process.

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