Fig 1.
sEMG and video data were collected simultaneously. Video data was analysed using the OpenFace software to yield AUs. sEMG data were first filtered, separated to independent components using the fastICA algorithm, manually classified as sEMG or noise, manually associated to a specific FBB, converted to RMS and finally aligned to the AU data.
Fig 2.
Most prominent FBBs (black contours) sketched manually on typical heat-maps, projected on lateral photographs of the participants. Red indicates highest muscle activation and blue denotes the lowest.
Fig 3.
ICs and AUs data during a calibration step of participant MD8040.
(a) ICs numbered according to the FBBs analysis depicted at Fig 1. (b) Typical AUs derived from video data recorded simultaneously with the sEMG recordings. Grey signals indicate muscle activation at the upper part of the face, while black, at the lower part.
Fig 4.
ICs data reconstruction from AUs data using multiple linear regression.
(a) Top: Moving IC-IX RMS signal of participant MD8040 (black) and its reconstruction signal (grey) using MLR, where the AUs are the explanatory variables (P value (<<) 0.001, F test = 815, (R2) = 0.78, Error Variation = 0.005). Bottom: moving IC-II RMS signal of participant SC8039 and its reconstruction signal (P value (<<) 0.001, F test = 842, (R2) = 0.79, Error Variation = 0.006) (b) MLR’s weights of different participants for the reconstruction of IC-IX (top) and IC-II (bottom), color-coded in grey level: white indicates the most influential weight for a specific IC, while black indicates the lesser weight (P value (<<) 0.001, F test (>) 200, average of (R2) = 0.72, Error Variation (<) 0.016 for all MLR reconstructions).
Fig 5.
AUs data reconstruction from ICs data using multiple linear regression.
(a) AUs data and the corresponding multiple linear regression reconstruction, where the ICs are the explanatory variables for a single participant during a calibration step. (b) Calculated reconstructions weights (see Methods section) for four different sessions (namely: Calibration—repetition 1 (2 min), Calibration—repetition 2 (2 min), Imitation (5 min) and Spontaneous smile (16 min) for participant MD8040), color-coded in grey level: white indicates the most influential weight for a specific AU, while black indicates the lesser weight.(P value (<<) 0.001, F test (>) 117, average of (R2) = 0.56, average of Error Variation = 0.13 for all MLR reconstructions).
Fig 6.
ICs and AUs data during spontaneous smiles.
ICs corresponding to six specific FBBs (FBB-I, IV, VI, VIII, II and X; left) and two AUs (AU12 and AU6; right) data of eight spontaneous smiles ordered from highest (top) to lowest (bottom) amplitude (sorted according to IC-I) for participant NS8034. ICs corresponding to the upper and lower parts of the face are plotted in grey and black, respectively.