Sentence examples for eeg samples from inspiring English sources

We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52).

Such behavior means that there is a high temporal correlation between a given EEG sample and neighboring EEG samples as well as some relatively distant EEG samples.

A feature vector, consisting of five designated EEG samples (close to the 300 ms) and the correlation coefficient (i.e., the similarity) was used (N = 6).

McSharry et al. [8] discussed and enumerated the nonlinear methods and its relevance to predict epilepsy by considering EEG samples as time series.

Also, an example of a possible feature vector consisting of a set of EEG samples in the vicinity of 300 ms, say, from the 57th to the 65th samples, given the sampling rate 240 Hz, is indicated in red color.

Considering (2), we set feature values F1 ~ F5 to the EEG samples as well as F6 to the correlation coefficient R measured between the EEG segment and the P300 template.

Unfortunately, the detection rate did not satisfactorily reach to a sufficiently high accuracy level, thus implying that features directly selected from the raw EEG sample data might be inadequate for such a detection problem.

Intracranial EEG sampling was guided by analysis of seizure symptoms, fluorodeoxyglucose-PET and MEG data.

At least once per hour a one-minute artefact-free raw EEG sample (10-second epoch) of the subject lying awake with his eyes closed was selected for further quantitative analysis.

In each case, a 10 minutes long digital raw EEG sample was exported and power spectral density (PSD) was computed by Fast Fourier Transformation [ 12, 13] based on twenty randomly selected 6 seconds long periods.

In this study, the following parameters have been used: EEG model: Sampling frequency = 32 Hz, EEG epoch length = 30 s, and SBR = 10 dB.