TY - JOUR
T1 - Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis
AU - Fitzgibbon, Sean
AU - DeLosAngeles, Dylan
AU - Lewis, Trent
AU - Powers, David
AU - Grummett, Tyler
AU - Whitham, Emma
AU - Ward, Lawrence
AU - Willoughby, John
AU - Pope, Kenneth
PY - 2016/3
Y1 - 2016/3
N2 - Objective: Validate independent component analysis (ICA) for removal of EMG contamination from EEG, and demonstrate a heuristic, based on the gradient of EEG spectra (slope of graph of log EEG power vs log frequency, 7–70 Hz) from paralysed awake humans, to automatically identify and remove components that are predominantly EMG. Methods: We studied the gradient of EMG-free EEG spectra to quantitatively inform the choice of threshold. Then, pre-existing EEG from 3 disparate experimental groups was examined before and after applying the heuristic to validate that the heuristic preserved neurogenic activity (Berger effect, auditory odd ball, visual and auditory steady state responses). Results: (1) ICA-based EMG removal diminished EMG contamination up to approximately 50 Hz, (2) residual EMG contamination using automatic selection was similar to manual selection, and (3) task-induced cortical activity remained, was enhanced, or was revealed using the ICA-based methodology. Conclusion: This study further validates ICA as a powerful technique for separating and removing myogenic signals from EEG. Automatic processing based on spectral gradients to exclude EMG-containing components is a conceptually simple and valid technique. Significance: This study strengthens ICA as a technique to remove EMG contamination from EEG whilst preserving neurogenic activity to 50 Hz.
AB - Objective: Validate independent component analysis (ICA) for removal of EMG contamination from EEG, and demonstrate a heuristic, based on the gradient of EEG spectra (slope of graph of log EEG power vs log frequency, 7–70 Hz) from paralysed awake humans, to automatically identify and remove components that are predominantly EMG. Methods: We studied the gradient of EMG-free EEG spectra to quantitatively inform the choice of threshold. Then, pre-existing EEG from 3 disparate experimental groups was examined before and after applying the heuristic to validate that the heuristic preserved neurogenic activity (Berger effect, auditory odd ball, visual and auditory steady state responses). Results: (1) ICA-based EMG removal diminished EMG contamination up to approximately 50 Hz, (2) residual EMG contamination using automatic selection was similar to manual selection, and (3) task-induced cortical activity remained, was enhanced, or was revealed using the ICA-based methodology. Conclusion: This study further validates ICA as a powerful technique for separating and removing myogenic signals from EEG. Automatic processing based on spectral gradients to exclude EMG-containing components is a conceptually simple and valid technique. Significance: This study strengthens ICA as a technique to remove EMG contamination from EEG whilst preserving neurogenic activity to 50 Hz.
KW - Alpha rhythms
KW - Electroencephalogram
KW - Electromyogram
KW - Neuromuscular paralysis
KW - Oddball
KW - Photic stimulation
KW - Steady state responses
UR - http://www.scopus.com/inward/record.url?scp=85027929173&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2015.12.009
DO - 10.1016/j.clinph.2015.12.009
M3 - Article
SN - 1388-2457
VL - 127
SP - 1781
EP - 1793
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
IS - 3
ER -