Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing

• Autorzy: Kotowski K. , Stapor K. , Leski J. , Kotas M.

Identyfikatory

DOI

10.1016/j.bbe.2018.06.006

• Języki publikacji: EN

Abstrakty

EN

The article presents our proposed adaptation of the commercially available Emotiv EPOC+ EEG headset for neuroscience research based on event-related brain potentials (ERP). It solves Emotiv EPOC+ synchronization problems (common to most low-cost systems) by applying our proposed stimuli marking circuit. The second goal was to check the capabilities of our modification in neuroscience experiments on emotional face processing. Results of our experiment show the possibility of measuring small differences in the early posterior negativity (EPN) component between neutral and emotional (angry/happy) stimuli consistently with previous works using research-grade EEG systems.

Słowa kluczowe

EN

electroencefalography; Emotiv EPOC+; ERP; early posterior negativity; emotional facial expression

PL

elektroencefalografia; Emotiv EPOC+; ERP; wyraz twarzy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 4
• Strony: 773--781
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Kotowski K.

• Institute of Informatics, Silesian University of Technology, Gliwice, Poland

autor

Stapor K.

• Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

autor

Leski J.

• Institute of Electronics, Silesian University of Technology, Gliwice, Poland

autor

Kotas M.

• Institute of Electronics, Silesian University of Technology, Gliwice, Poland

Bibliografia

• [1] Luck SJ. Ten simple rules for designing and interpreting ERP experiments. Event-related potentials: a methods handbook. The MIT Press; 2004. p. 17–32.
• [2] Rhodes G, Calder A, Johnson M, Haxby JV, Eimer M. The face-sensitive N170 component of the event-related brain potential. Oxford handbook of face perception; 2011, http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199559053.001.0001/oxfordhb-9780199559053-e-017.
• [3] Eimer M, Holmes A. Event-related brain potential correlates of emotional face processing. Neuropsychologia 2007;45 (1):15–31. http://dx.doi.org/10.1016/j.neuropsychologia.2006.04.022.
• [4] Emotiv. EMOTIV EPOC & TESTBENCH specifications; 2014, https://www.emotiv.com/files/Emotiv-EPOC-Product-Sheet-2014.pdf.
• [5] Hairston WD, Whitaker KW, Ries AJ, Vettel JM, Bradford JC, Kerick SE, et al. Usability of four commercially-oriented EEG systems. J Neural Eng 2014;11(4):046018, http://stacks.iop.org/1741-2552/11/i=4/a=046018.
• [6] Badcock NA, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G. Validation of the Emotiv EPOC EEG gaming system for measuring research quality auditory ERPs. PeerJ 2013;1:e38. http://dx.doi.org/10.7717/peerj.38.
• [7] Lissa D, Sörensen S, Badcock N, Thie J, McArthur G. Measuring the face-sensitive N170 with a gaming EEG system: a validation study. J Neurosci Methods 2015;253:47–54. http://dx.doi.org/10.1016/j.jneumeth.2015.05.025.
• [8] Badcock NA, Preece KA, de Wit B, Glenn K, Fieder N, Thie J, et al. Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children. PeerJ 2015;3:e907. http://dx.doi.org/10.7717/peerj.907.
• [9] Thie J. A wireless marker system to enable evoked potential recordings using a wireless EEG system (EPOC) and a portable computer. PeerJ PrePrints 2013;1:e32v1. http://dx.doi.org/10.7287/peerj.preprints.32v1.
• [10] Wronka E, Walentowska W. Attention modulates emotional expression processing. Psychophysiology 2011;48(8):1047–56. http://dx.doi.org/10.1111/j.1469-8986.2011.01180.x.
• [11] Rellecke J, Sommer W, Schacht A. Emotion effects on the N170: a question of reference? Brain Topogr 2013;26(1):62–71. http://dx.doi.org/10.1007/s10548-012-0261-y.
• [12] Olofsson JK, Nordin S, Sequeira H, Polich J. Affective picture processing: an integrative review of ERP findings. Biol Psychol 2008;77(3):247–65. http://dx.doi.org/10.1016/j.biopsycho.2007.11.006.
• [13] Carrillo I, Meza-Kubo V, Morn AL, Galindo G, García-Canseco E. Emotions identification to measure user experience using brain biometric signals. In: Zhou J, Salvendy G, editors. Human aspects of IT for the aged population. Design for aging. Springer International Publishing; 2015. p. 15–25.
• [14] Ramirez R, Vamvakousis Z. Detecting emotion from EEG signals using the emotive EPOC device. In: Zanzotto FM, Tsumoto S, Taatgen N, Yao Y, editors. Brain informatics. Springer Berlin Heidelberg; 2012. p. 175–84.
• [15] Yi S, He W, Zhan L, Qi Z, Zhu C, Luo W, et al. Emotional noun processing: an ERP study with rapid serial visual presentation. PLOS ONE 2015;10(3):e0118924. http://dx.doi.org/10.1371/journal.pone.0118924.
• [16] Zhang D, He W, Wang T, Luo W, Zhu X, Gu R, et al. Three stages of emotional word processing: an ERP study with rapid serial visual presentation. Soc Cognit Affect Neurosci 2014;9(12):1897–903. http://dx.doi.org/10.1093/scan/nst188.
• [17] Sato W, Kochiyama T, Yoshikawa S, Matsumura M. Emotional expression boosts early visual processing of the face: ERP recording and its decomposition by independent component analysis.. Neuroreport 2001;12(4):709–14.
• [18] Batty M, Taylor MJ. Early processing of the six basic facial emotional expressions. Cognit Brain Res 2003;17(3):613–20. http://dx.doi.org/10.1016/S0926-6410(03)00174-5.
• [19] Muller-Bardorff M, Schulz C, Peterburs J, Bruchmann M, Mothes-Lasch M, Miltner W, et al. Effects of emotional intensity under perceptual load: an event-related potentials (ERPs) study. Biol Psychol 2016;117:141–9. http://dx.doi.org/10.1016/j.biopsycho.2016.03.006.
• [20] Schindler S, Zell E, Botsch M, Kissler J. Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory. Sci Rep 2017;7:45003. http://dx.doi.org/10.1038/srep45003.
• [21] Schupp H, Flaisch T, Stockburger J, Junghöfer M. Emotion and attention: event-related brain potential studies. Prog Brain Res 2006;156:31–51.
• [22] Wieser M, Moscovitch D. The effect of affective context on visuocortical processing of neutral faces in social anxiety. Front Psychol 2015;6.
• [23] Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A. Review and classification of emotion recognition based on EEG brain–computer interface system research: a systematic review; 2017.
• [24] Pham TD, Tran D. Emotion recognition using the Emotiv EPOC device. In: Huang T, Zeng Z, Li C, Leung CS, editors. Neural information processing. Springer Berlin Heidelberg; 2012. p. 394–9.
• [25] Lopez-Gil J-M, Virgili-Goma J, Gil R, Guilera T, Batalla I, Soler-Gonzalez J, et al. Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way. Front Comput Neurosci 2016;10:85. http://dx.doi.org/10.3389/fncom.2016.00085.
• [26] Meza-Kubo V, Moran AL, Carrillo I, Galindo G, Garcia-Canseco E. Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals. J Biomed Inform 2016;62:202–9. http://dx.doi.org/10.1016/j.jbi.2016.07.004.
• [27] Duvinage M, Castermans T, Petieau M, Hoellinger T, Cheron G, Dutoit T. Performance of the Emotiv Epoc headset for P300-based applications. BioMed Eng OnLine 2013;12(1):56. http://dx.doi.org/10.1186/1475-925X-12-56.
• [28] Hairston WD. Accounting for timing drift and variability. Contemporary Electroencepholography (EEG) Systems. Army Research Laboratory (ARL-TR-5945); 2012. http://dx.doi.org/10.21236/ADA561715.
• [29] Vishay. BPW34, BPW34s Product Sheet; August 2011, https://www.vishay.com/docs/81521/bpw34.pdf.
• [30] Aifanti N, Papachristou C, Delopoulos A. The MUG facial expression database. 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10. 2010. pp. 1–4.
• [31] American Electroencephalographic Society guidelines for standard electrode position nomenclature. J Clin Neurophysiol 1991;8(2), https://journals.lww.com/clinicalneurophys/Fulltext/1991/04000/American_Electroencephalographic_Society.7.aspx.
• [32] Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, et al. OpenViBE: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence Teleoper Virt Environ. 2010;19(1):35–53. http://dx.doi.org/10.1162/pres.19.1.35.
• [33] Ludwig KA, Miriani RM, Langhals NB, Joseph MD, Anderson DJ, Kipke DR.Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J Neurophysiol 2009;101(3):1679–89. http://dx.doi.org/10.1152/jn.90989.2008.
• [34] Gustafsson F. Determining the initial states in forward-backward filtering. IEEE Trans Signal Process 1996;44(4):988–92. http://dx.doi.org/10.1109/78.492552.
• [35] Frankiewicz Z, Leski J. Adaptive fiducial point detector for ECG stress testing systems.. Int J Biomed Comput 1991; 28(1-2):127–35.
• [36] Du P, Kibbe WA, Lin SM. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006;22(17):2059–65. http://dx.doi.org/10.1093/bioinformatics/btl355.
• [37] Ricker N. The form and laws of propagation of seismic wavelets. Geophysics 1953;18(1):10–40. http://dx.doi.org/10.1190/1.1437843.
• [38] Kotas M, Pander T, Leski JM. Averaging of nonlinearly aligned signal cycles for noise suppression. Biomed Signal Process Control 2015;21:157–68. http://dx.doi.org/10.1016/j.bspc.2015.06.003.
• [39] O'Regan S, Faul S, Marnane W. Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals. Med Eng Phys 2013;35(7):867–74. http://dx.doi.org/10.1016/j.medengphy.2012.08.017.
• [40] O'Regan S, Marnane W. Multimodal detection of head-movement artefacts in EEG. J Neurosci Methods 2013; 218(1):110–20. http://dx.doi.org/10.1016/j.jneumeth.2013.04.017.
• [41] Leski J. Robust weighted averaging [of biomedical signals]. IEEE Trans Biomed Eng 2002;49(8):796–804. http://dx.doi.org/10.1109/TBME.2002.800757.
• [42] Giroldini W, Pederzoli L, Bilucaglia M, Melloni S, Tressoldi P. A new method to detect event-related potentials based on Pearson's correlation. EURASIP J Bioinf Syst Biol 2016;2016:11. http://dx.doi.org/10.1186/s13637-016-0043-z.
• [43] Singh MI, Singh M. Development of a real time emotion classifier based on evoked EEG. Biocybern Biomed Eng 2017;37(3):498–509. http://dx.doi.org/10.1016/j.bbe.2017.05.004.
• [44] Ekman P. Basic emotions. Handbook of cognition and emotion. Wiley-Blackwell; 2005. p. 45–60. http://dx.doi.org/10.1002/0470013494.ch3.
• [45] Chmielnicki W, Stapor K. Using the one-versus-rest strategy with samples balancing to improve pairwise coupling classification. AMCS 2016;26(1):191. http://dx.doi.org/10.1515/amcs-2016-0013.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).