Sentence examples for offline filtering from inspiring English sources

The data were analyzed without additional offline filtering.

Experiment 1 considers an adaptive identification of the nonlinear systems according to Section 5.2.1, and experiment 2 considers an offline filtering without further adaptation with previously adapted models from the end of experiment 1.

The system provided a strong signal-to-noise ratio that did not require offline filtering (see unfiltered recording sequence in Figure 1).

The processing steps were comprised of artifact correction, offline filtering at an 80-Hz low pass (24 dB/oct) and a 0.3-Hz high pass (6 dB/oct) and artifact rejection (gradient>14.6 µV/ms, peak-to-peak amplitude>120 µV/ms).

No offline filtering was performed.

Additional offline filtering of 1 kHz was applied to the recordings for display.

The cycles of movement were later identified based on the offline filtered accelerometer signal (Fig. 1B, C).

The accelerometer signal was continuously sampled at 1.25 kHz and was offline filtered (2 Hz six-pole low-pass Butterworth filter) in order to focus on movement frequency rather than on the high frequency artifacts of the measurements.

Before applying Independent Components Analysis, the data were offline filtered by means of a high (1 Hz cut-off frequency) and low pass (25 Hz cut-off frequency) Finite Impulse Response filter implemented in EEGLAB (pop_ eegfilter function).

Responses were digitally sampled at 20,000 Hz, offline filtered from 70 to 2000 Hz with a 12 dB roll-off and epoched from -40 to 190 ms (stimulus onset at time zero).

The EEG data was processed offline to filter out MR artifacts and to remove ballistocardiogram artifacts (Brain Vision Analyzer 2.0, Germany).