Validation of melomind(TM) signal quality: a proof of concept resting-state and ERPs study
Spinelli, G.; Odouard, A.; Nierat, M.-C.; Campion, S.; Bensoussan, M.; Grosselin, F.; Pandremmenou, K.; Breton, A.; Raux, M.; Attal, Y.; Similowski, T.; Navarro-Sune, X.
Show abstract
Wearable EEG systems have become accessible to researchers and clinicians over the last decade, thus requiring neurotechnology companies to seek for outstanding EEG signal quality. Here, we show that the melomind headset equipped with dry electrodes (myBrain Technologies, Paris, France) allows the recording of reliable electro-cortical dynamics as compared to a wet-based standard-EEG system (actiCAP, Brain Products GmbH, Gilching, Germany). EEGs were acquired simultaneously from the two systems while thirteen subjects underwent a visual oddball, a steady-state visually-evoked potentials (SSVEPs) and two resting-state (RS) tasks. RS were acquired with eyes-closed and eyes-open (2 minutes each) and repeated twice (before and after the cognitive tasks). During the oddball task, participants responded on a gamepad when a target-stimulus was displayed. In the SSVEPs, visual responses were elicited at 15 and 20 Hz through a series of 15-second stimuli presented 5 times each. The power of theta- [4-8 Hz], alpha- [8-13 Hz], and beta- [13-30 Hz] band was extracted from the two RS. The signal-to-noise-ratio in the 15 ({+/-} 1) and 20 ({+/-} 1) Hz range was computed from the SSVEPs. The shape of the N2/P300 complex was analysed from the oddball task. Strong correlations resulted between the parameters obtained from the two EEG systems (0.53 < Pearsons r < 0.97). Bland and Altman analysis revealed small dissimilarities between the two systems, with values laying in the 95% confidence interval in all the tasks. Our results demonstrate that the melomind is an affordable solution to reliably assess humans electro-cortical dynamics at-rest and during cognitive tasks, thus paving the way to its use in neuroscience studies and brain-computer interfaces.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Qualitative and Quantitative Comparative Analysis of Common Normal Variants and Physiological Artifacts in MEG and EEG 96%
- Distinct spectral profiles of awake resting EEG in disorders of consciousness - the role of frequency and topography of oscillations 94%
- Automatic brain categorization of discrete auditory emotion expressions 94%
Similar papers in this journal
- Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments 97%
- EEG alpha-theta dynamics during mind wandering in the context of breath focus meditation: an experience sampling approach with novice meditation practitioners 96%
- Time course of EEG complexity reflects attentionalengagement during listening to speech in noise 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.