Abstract
Electroencephalography (EEG) has been the focus of research and advances for many years, yet there are several tasks to be explore and methods to be tested to improve analysis and classification. Event-Related Potential (ERP) is one of the brain responses measured with EEG, resulting from motor tasks usually are related to motor imagery or real movement. This study aims to analyze and classify event-related desynchronization (ERD) and event-related synchronization (ERS) occurred in tasks involving passive mobilization in Intensive Care Unit (ICU) sedated patients and non-sedated volunteers. Our main goal is to provide preliminary analysis and comparisons between sedated and non-sedated groups based on signal visualization and a classifier. Common Spatial Pattern filtering (CSP) and visual inspection of best band and time were used to verify signal and phenomena. From that, specific features (i.e., Root Mean Square, standard deviation, mean of Welch periodogram and differential entropy) were extracted based on time and frequency to apply a Linear Discriminant Analysis (LDA) classifier. Once the two Intensive Care Unit sedated patients and the two volunteers were analyzed, it was possible to observe the proposed phenomena. Mean accuracy in the best scenario and best person for each group (two people in each group) was found higher than 80 and 77% to sedated and non-sedated participants, respectively. Preliminary results, based on four participants (i.e., two sedated and two non-sedated patients), suggested lateralization in tasks performed with passive mobilization and provided accuracy comparable to previous studies involving motor tasks.
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References
Dornhege G (2007) Toward brain-computer interfacing. MIT, London, 1
Muller-Putz GR, Pfurtscheller G (2007) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55:361–364
Carra M (2012) Desenvolvimento de uma interface cérebro computador baseada em ritmos sensério motores para controle de dispositivos Master’s thesis. Universidade Federal do Rio Grande do Sul, Porto Alegre
Czyżewski A, Kurowski A, Odya P, Szczuko P (2020) Multifactor consciousness level assessment of participants with acquired brain injuries employing human-computer interfaces. Biomed Eng Online 19:1–26
Niedermeyer E, Silva FHL (2005) Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins
Sanei S, Chambers J (2013) EEG signal processing. Wiley, New York
Nam C, Jeon Y, Kim YJ et al. Movement imagery-related lateralization of event-related (de) synchronization (ERD/ERS): motor-imagery duration effects. Clinical Neurophysiol 122:567–577
Pfurtscheller G, Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiol 110:1842–1857
Stock VN, Balbinot A (2016) Movement imagery classification in EMOTIV cap based system by Naïve Bayes. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 4435–4438
BCI Competition III at http://www.bbci.de/competition/ii/
Carra M (2012) Development of brain computer interface based on sensory motor rhythms for device control. Master’s thesis. Federal University of Rio Grande do Sul, Porto Alegre
Doyle LMF, Yarrow K, Brown P (2005) Lateralization of event-related beta desynchronization in the EEG during pre-cued reaction time tasks. Clinical Neurophysiol 116:1879–1888
Machado J, Balbinot A (2014) Executed movement using EEG signals through a Naive Bayes classifier. Micromachines. 5:1082–1105
Blankertz B, Tomioka R, Lemm S et al (2007) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Magz 25:41–56
Alzahab NA, Alimam H, Alnahhas MHD et al (2019) Determining the optimal feature for two classes Motor-Imagery Brain-Computer Interface (L/R-MI-BCI) systems in different binary classifiers. Int J Mech Mechatron Eng IJMME–IJENS 19:132–150
Stoica P, Mosesand R (2005) Spectral analysis of signals
Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust. 15:70–73
Shi LC, Jiao YY, Lu BL (2013) Differential entropy feature for EEG-based vigilance estimation. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 6627–6630
Bashashati H, Ward R, Birch G et al (2015) Comparing different classifiers in sensory motor brain computer interfaces. PloS one. 10:e012940129435
Acknowledgements
We thank Alexandre Simões Dias (Hospital de Clínicas de Porto Alegre - HCPA), Luiz Alberto Forgiarini Junior and Rodrigo Noguera for help and assistance in ICU data acquisition, passive mobilization discussions and contact with hospitals.
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Florisbal, G.C., Machado, J., Bagesteiro, L.B., Balbinot, A. (2022). Analysis and Classification of EEG Signals from Passive Mobilization in ICU Sedated Patients and Non-sedated Volunteers. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_127
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DOI: https://doi.org/10.1007/978-3-030-70601-2_127
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