detect_artifacts: Detect artifacts in fEMG

In SDAMcIntyre/countenance: Read, process and analyse facial EMG data

Description

Artifact rejection procedure as described in Künecke et. al. (2014). Data samples are flagged if the range within a sliding 50ms window exceeds three times the standard deviation of the full session recording.

Usage

detect_artifacts(
  femg_data,
  muscle_channels,
  win_sec = 0.05,
  flag_threshold = 3
)

Arguments

femg_data	data frame or tibble
muscle_channels	A vector with the names of the channels in the data that contain EMG data to be checked for artifacts.
win_sec	0.05 sec by default. The duration of the sliding window in which to look for extreme values.
flag_threshold	3 by default. The multiple of SD beyond which the range of data in win_sec is considered a likely artifact.

Details

Künecke, J., Hildebrandt, A., Recio, G., Sommer, W., & Wilhelm, O. (2014). Facial EMG Responses to Emotional Expressions Are Related to Emotion Perception Ability. PLoS ONE, 9(1), e84053. https://doi.org/10.1371/journal.pone.0084053

Value

data frame or tibble with additional variables:

(muscle_channel)_z: z transformation of the muscle data

(muscle_channel)_zrange: range of the window of duration win_sec centred on this sample

(muscle_channel)_flagged: boolean indicating if the window centered on this sample contains artifactual data

(muscle_channel)_fixed: same data as muscle_channel but flagged samples are set to NA

(muscle_channel)_zfixed: same data as muscle_channel_z but flagged samples are set to NA

SDAMcIntyre/countenance documentation built on Dec. 18, 2021, 11:58 a.m.