Measurement of the N170 during facial neuromuscular electrical stimulation (fNMES)

Abstract

Facial Neuromuscular Electrical Stimulation (fNMES) allows for a controlled influence of contractions of facial muscles, and may be used to advance our understanding of the impact of facial muscle activations on the experience and perception of affect (facial feedback effects), especially when combined with brain-recording methods such as Electroencephalography (EEG). However, electrical stimulation introduces significant electromagnetic interference into the EEG signal that can mask underlying brain dynamics.Whether established signal processing methods can allow for a reduction of said interference whilst retaining effects of interest, remains unexplored. We addressed these questions focusing on the classic N170 visual evoked potential, a face-sensitive brain component: 20 participants viewed images of houses, and of sad, happy, and neutral faces, whilst performing an irrelevant target detection task. On half of the trials, fNMES was delivered for 500 milliseconds to bilateral lower-face muscles during the presentation of visual stimuli. As expected, a larger N170 amplitude was found for faces relative to houses. Interestingly, this was the case both without and during fNMES, regardless of whether the fNMES artefact was removed or not. Moreover, sad facial expressions elicited a larger N170 amplitude relative to neutral facial expressions, both with and without fNMES. We show that the combining of fNMES and EEG can be achieved and may serve as a powerful means of exploring the impact of controlled proprioceptive inputs on various types of cognitive processing.

Zygomaticus activation through facial neuromuscular electric stimulation (fNMES) induces happiness perception in ambiguous facial expressions and affects neural correlates of face processing

“…The EEG data were analysed in MATLAB using the EEGLAB toolbox (Delorme & Makeig, 2004). We followed a previously established procedure for the cleaning of fNMESinduced artefacts (Baker et al, 2023). All 650 trials were filtered with a 0.5 Hz high pass and 80 Hz low pass, channels with excessive noise or artefacts were identified through visual inspection and then interpolated, line noise was removed using Zapline and Cleanline, and data was epoched from 500 ms before to 800 ms after stimulus onset.…”

“…All 650 trials were filtered with a 0.5 Hz high pass and 80 Hz low pass, channels with excessive noise or artefacts were identified through visual inspection and then interpolated, line noise was removed using Zapline and Cleanline, and data was epoched from 500 ms before to 800 ms after stimulus onset. We then performed Independent Component Analysis (ICA) on the data using the runica function in EEGLAB (Delorme and Makeig, 2004), and removed components representing blinks and fNMES artefacts (see Baker et al, 2023 for a detailed description of this approach). Trials were labelled for rejection if values in the pre-stimulus baseline for any channel exceeded +/-100 µV.…”