Abstract
Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain–computer interfaces, e.g., different limb imaginations.
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Birbaumer N, Cohen LG (2007) Brain–computer interfaces: communication and restoration of movement in paralysis. J Physiol 579(3):621–636
Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller K-R, Curio G, Dickhaus T (2010) Neurophysiological predictor of SMR-based BCI performance. Neuroimage 51(4):1303–1309
Carlson T, Demiris Y (2012) Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload. IEEE Trans Syst Man Cybern Part B Cybern 42(3):876–888
Casson AJ, Yates DC, Smith SJ, Duncan JS, Rodriguez-Villegas E (2010) Wearable electroencephalography. IEEE Eng Med Biol Mag 29(3):44–56
Cherkassky V (1997) The Nature Of Statistical Learning Theory~. IEEE Trans Neural Netw 8(6):1564
Contreras-Vidal JL, Grossman RG (2013) NeuroRex: a clinical neural interface roadmap for EEG-based brain machine interfaces to a lower body robotic exoskeleton. In: Conference proceedings: annual international conference of The IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. Annual conference [Conf Proc IEEE Eng Med Biol Soc], 2013
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21
Fu Y, Xiong X, Jiang C, Xu B, Li Y, Li H (2017) Imagined hand clenching force and speed modulate brain activity and are classified by NIRS combined With EEG. IEEE Trans Neural Syst Rehabil Eng 25(9):1641–1651
Leeb R, Tonin L, Rohm M, Desideri L, Carlson T, Millan JDR (2015) Towards independence: a BCI telepresence robot for people with severe motor disabilities. Proc IEEE 103(6):969–982
Long J, Tazoe T, Soteropoulos DS, Perez MA (2016) Interhemispheric connectivity during bimanual isometric force generation. J Neurophysiol 115(3):1196–1207
Ma Y, Ding X, She Q, Luo Z, Potter T, Zhang Y (2016) Classification of motor imagery EEG signals with support vector machines and particle swarm optimization. Comput Math Methods Med
Millán JD, Galán F, Vanhooydonck D, Lew E, Philips J, Nuttin M (2009) Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society. annual conference [Conf Proc IEEE Eng Med Biol Soc], 2009
Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R (2005) EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett 382(1–2):169–174
Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain–computer communication. Proc IEEE 89(7):1123–1134
Pfurtscheller G, Neuper C, Guger C, Harkam W, Ramoser H, Schlogl A, Obermaier B, Pregenzer M (2000) Current trends in Graz brain–computer interface (BCI) research. IEEE Trans Rehabil Eng 8(2):216–219
Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446
Tavella M, Leeb R, Rupp R, Millán JDR (2010) Towards natural non-invasive hand neuroprostheses for daily living. In: 2010 Annual international conference of the IEEE engineering in medicine and biology, pp 126–129
Vuckovic A, Sepulveda F (2008) Delta band contribution in cue based single trial classification of real and imaginary wrist movements. Med Biol EngComput 46(6):529–539
Wolpaw JR (2007) Brain–computer interfaces as new brain output pathways. J Physiol 579(3):613–619
Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173
Xu L, Xu M, Jung TP, Ming D (2021) Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface [published correction appears in Cogn Neurodyn. 2021 Oct;15(5):921]. Cogn Neurodyn 15(4):569–584
Yan W, Xu G (2020) Brain–computer interface method based on light-flashing and motion hybrid coding. Cogn Neurodyn 14(5):697–708
Acknowledgements
We acknowledge the participants in the study. This study was supported by funding from the National Natural Science Foundation of China (No. 61773179), the Outstanding Youth Project of Guangdong Natural Science Foundation of China (No. 2021B1515020076), the Guangdong Provincial Natural Science Foundation of China (No. 2019A1515012175), and the China Southern Power Grid (Grant No. GDKJXM20185761).
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Hualiang, L., Xupeng, Y., Yuzhong, L. et al. A novel noninvasive brain–computer interface by imagining isometric force levels. Cogn Neurodyn 17, 975–983 (2023). https://doi.org/10.1007/s11571-022-09875-2
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DOI: https://doi.org/10.1007/s11571-022-09875-2