Abstract
Beta has been scarcely defined as a potential feature for motor imagery-based Brain-Computer Interface use due to its unstable nature in the temporal and spectral domain. Specifying or narrowing down beta sub-bands cannot detect beta occurring non-uniformly in time and frequency for different tasks. This study aims to evaluate beta response quantitatively in terms of event-related synchronization during 3 continuous stages (pre-task, on-task and post-task) of each single imagery trial. Electroencephalogram of thirteen healthy college-aged students were measured on 3 channels (C3, Cz and C4) while the subject performs 3 imagery movements (left hand, right hand and feet). The study proposes an alternative statistical approach utilizing standard event-related synchronization analysis and Fast Fourier Transform based on summarizing all measurement data sets. Broad beta power spectrum, varying from 13 to 35 Hz, is analyzed in order to get trial-channel-specific beta frequency response, and is visualized by using time–frequency color map. The new approach shows the mean percentage of beta response during on-task stage when combining all tasks are 21.8%, 23.1% and 21.1%, for 3 channels C3, Cz and C4, respectively. The results from ANOVA analysis shows that there is a significant difference (p < 0.0001) in the percentage of beta response during 3 continuous stages for all 3 tasks. Besides, feet imagery task has a difference in the percentage of beta response in 3 channels, while left/right hand task has no difference. Lastly, the most common beta frequency response is found to be different (p < 0.05) during 3 continuous stages for left hand task, while the right hand and feet task has no difference.
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References
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughana TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791
Scherer R, Lee F, Schlogl A, Leeb R, Bischof H, Pfurtscheller G (2008) Toward self-paced brain-computer communication: navigation through virtual worlds. IEEE Trans Biomed Eng 55(2):675–682
Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recogn Lett 22(12):1299–1309
Galán F, Nuttin M, Lew E, Ferrez PW, Vanacker G, Philips J, del Millán, JR (2008) A brain-actuated wheelchair: Asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol 119(9):2159–2169
Graimann B, Allison BZ, Pfurtscheller G (2010) Brain-computer interfaces: revolutionizing human-computer interaction. Springer, New York
Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 8(2):211–214
Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng 8(2):174–179
Li Y, Nam CS, Shadden BB, Johnson SL (2010) A P300-Based brain-computer interface: effects of interface type and screen size. Int J Human-Comput Inter 27(1):52–68
Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857
Neuper C, Müller-Putz GR, Scherer R, Pfurtscheller G (2006) Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog Brain Res 159:393–409
Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G (2009) Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin Neurophysiol 120(2):239–247
Neuper C, Scherer R, Reiner M, Pfurtscheller G (2005) Imagery of motor actions: differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG. Cogn Brain Res 25(3):668–677
Neuper C, Pfurtscheller G (2001) Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas. Clin Neurophysiol 112(11):2084–2097
Pfurtscheller G, Neuper Ch, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103(6):642–651
Neuper C, Pfurtscheller G (1998) ERD/ERS based brain computer interface (BCI): effects of motor imagery on sensorimotor rhythms. Int J Psychophysiol 30:53–54
Pfurtscheller G, Neuper C (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239(2–3):65–68
Pfurtscheller G, Neuper C, Brunner C, Lopes da Silva F (2005) Beta rebound after different types of motor imagery in man. Neurosci Lett 378:156–159
Alegre M, Labarga A, Gurtubay IG, Iriarte J, Malanda A, Artieda J (2002) Beta electroencephalograph changes during passive movements: sensory afferences contribute to beta event-related desynchronization in humans. Neurosci Lett 331(1):29–32
Pfurtscheller G, Neuper C, Pichler-Zalaudek K, Edlinger G, Lopes da Silva FH (2000) Do brain oscillations of different frequencies indicate interaction between cortical areas in humans? Neurosci Lett 286(1):66–68
Jasper HH (1958) The ten-twenty electrode system of the International Federation. Electroencephalogr Clin Neurophysiol 10:370–375
Cinar E, Sahin F (2013) New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot. Neural Comput Appl 22:29–39
Kaiser V, Kreilinger A, Müller-Putz GR, Neuper C (2011) First steps toward a motor imagery based stroke BCI: new strategy to set up a classifier. Front Neurosci 5:86
Jeon Y, Namb CS, Kim Y-J, Whang MC (2011) Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: implications for brain-computer interfaces. Int J Ind Ergon 41:428–436
Acknowledgements
We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU—HCM for this study.
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Nguyen, M.T.D., Pham, C.Q., Nguyen, H.N., Le, K.Q., Huynh, L.Q. (2022). A Statistical Approach to Evaluate Beta Response in Motor Imagery-Based Brain-Computer Interface. In: Van Toi, V., Nguyen, TH., Long, V.B., Huong, H.T.T. (eds) 8th International Conference on the Development of Biomedical Engineering in Vietnam. BME 2020. IFMBE Proceedings, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-75506-5_16
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