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Giulia Cisotto edited this page Jun 25, 2021 · 3 revisions

EEG-EMG-analytics

This repository contains a set of Matlab scripts to process EEG and EMG signals (feature extraction, spectral analysis).

You can extract the most common expert features from your EEG or EMG signal, both in the time and in the frequency domain.

Time domain features:

  • MIN: min amplitude value
  • MAX: max amplitude value
  • MEAN: mean amplitude value
  • MED: median amplitude value
  • SD: standard deviation from the mean
  • VAR: variance from the mean
  • PP: peak-to-peak distance (range)
  • ZC: zero-crossings
  • AUC: area under curve
  • RMS: root mean square (entire segment)
  • MP: mean (amplitude) power
  • MAV: mean absolute value
  • WL: waveform length
  • SK: skewness
  • KUR: kurtosis

Frequency domain features:

  • MNF: mean frequency (in power spectrum)
  • MDF: median frequency (in power spectrum)
  • SPC: spectral centroid
  • EN: signal's energy (area under the power spectrum curve) And only for EEG
  • BPd: band power in delta band (0.5,4) Hz
  • BPt: band power in theta band (4,8) Hz
  • BPa: band power in alpha band (8,13) Hz
  • BPb: band power in beta band (13,30) Hz
  • BPg: band power in gamma band >=30 Hz

References:

[1] Cisotto, G., Guglielmi, A. V., Badia, L., & Zanella, A. (2018, September). Classification of grasping tasks based on EEG-EMG coherence. In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). IEEE.

[2] Cisotto, G., Rosati, G., & Paccagnella, A. (2019, July). A simple and accessible inkjet platform for ultra-short concept-to-prototype sEMG electrodes production. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5765-5768). IEEE.

[3] Cisotto, G., Capuzzo, M., Guglielmi, A. V., & Zanella, A. (2020, June). Feature selection for gesture recognition in Internet-of-Things for healthcare. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.

[4] Rosati, G., Cisotto, G., Sili, D., Compagnucci, L., De Giorgi, C., Pavone, E., ... & Paccagnella, A. (2021). Inkjet-printed fully-customizable and low-cost electrodes matrix for gesture recognition.

[5] Cisotto, G. (2021, June). REPAC: Reliable estimation of phase-amplitude coupling in brain networks. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1075-1079). IEEE.

[6] Cisotto, G., Guglielmi, A. V., Badia, L., & Zanella, A. (2018, December). Joint Compression of EEG and EMG Signals for Wireless Biometrics. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.

[7] Cisotto, G., Michieli, U., & Badia, L. (2017). A coherence study on EEG and EMG signals. arXiv preprint arXiv:1712.01277.

[8] Cisotto, G., Pupolin, S., Silvoni, S., Cavinato, M., Agostini, M., & Piccione, F. (2013, June). Brain-computer interface in chronic stroke: an application of sensorimotor closed-loop and contingent force feedback. In 2013 IEEE International Conference on Communications (ICC) (pp. 4379-4383). IEEE.

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