Abstract
Brain-computer interfaces (BCIs) have been proposed for neurorehabilitation after stroke by inducing cortical plasticity. To transfer the technology from the controlled settings in the lab to the clinic several issues must be addressed. In this study, it was investigated how the performance was affected by using a universal task template to detect movement intentions associated with movements performed with two different levels of force and speed. The performance of the universal template was compared to an individualized template constructed for each task. Twelve healthy subjects performed four types of dorsi-flexions while continuous electroencephalography (EEG) was recorded from ten channels. The movement intentions were detected (~200–300 ms before the movement onset) from the continuous EEG using a matched-filter approach. The true positive rate was significantly higher (P = 0.001) when using the individualized template where 68–76 % of the movements were correctly detected on the contrary to 65–70 % when using the universal template. The number of false positive detections per 5 min was lower (P = 0.036) when using the universal template (~13) compared to the individualized template (~14). Despite the lower performance when using the universal detection template, the performance of the detector is in the range of what has been reported previously for inducing cortical plasticity.
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References
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Millán, J.R., Rupp, R., Müller-Putz, G.R., Murray-Smith, R., Giugliemma, C., Tangermann, M., Vidaurre, C., Cincotti, F., Kübler, A., Leeb, R., Neuper, C., Müller, K., Mattia, D.: Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges. Front. Neurosc. 1 (2010)
Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7, 1032–1043 (2008)
Grosse-Wentrup, M., Mattia, D., Oweiss, K.: Using brain-computer interfaces to induce neural plasticity and restore function. J. Neural Eng. 8, 025004 (2011)
Mrachacz-Kersting, N., Niazi, I.K., Jiang, N., Pavlovic, A., Radovanović, S., Kostic, V., Popovic, D., Dremstrup, K. Farina, D.: A novel brain-computer interface for chronic stroke patients. In: Converging Clinical and Engineering Research on Neurorehabilitation Anonymous, pp. 837–841. Springer (2013)
Pascual-Leone, A., Dang, N., Cohen, L.G., Brasil-Neto, J.P., Cammarota, A., Hallett, M.: Modulation of muscle responses evoked by transcranial magnetic stimulation during the acquisition of new fine motor skills. J. Neurophysiol. 74, 1037–1045 (1995)
Mrachacz-Kersting, N., Kristensen, S.R., Niazi, I.K., Farina, D.: Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity. J. Physiol. (Lond.), 590, 1669–1682 (2012)
Shibasaki, H., Hallett, M.: What is the Bereitschaftspotential? Clin. Neurophysiol. 117, 2341–2356 (2006)
Niazi, I.K., Jiang, N., Tiberghien, O., Nielsen, J.F., Dremstrup, K., Farina, D.: Detection of movement intention from single-trial movement-related cortical potentials. J. Neural Eng. 8, 066009 (2011)
Lew, E., Chavarriaga, R., Silvoni, S., Millán, J.R.: Detection of self-paced reaching movement intention from EEG signals. Front. Neuroeng. 5, 13 (2012)
Jochumsen, M., Niazi, I.K., Mrachacz-Kersting, N., Farina, D., Dremstrup, K.: Detection and classification of movement-related cortical potentials associated with task force and speed. J. Neural Eng. 10, 056015 (2013)
Jochumsen, M., Niazi, I.K., Rovsing, H., Rovsing, C., Nielsen, G.A., Andersen, T.K., Dong, N.P., Sørensen, M.E., Mrachacz-Kersting, N., Jiang, N., Farina, D., Dremstrup, K.: Detection of movement intentions through a single channel of electroencephalography. In Replace, Repair, Restore, Relieve–Bridging Clinical and Engineering Solutions in Neurorehabilitation Anonymous, pp. 465-472. Springer (2014)
Ang, K.K., Chua, K.S.G., Phua, K.S., Wang, C., Chin, Z.Y., Kuah, C.W.K., Low, W., Guan, C.: A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin. EEG Neurosci. (2014)
Ang, K.K., Guan, C.: Brain-Computer Interface in Stroke Rehabilitation. J. Comput. Sci.Eng. 7, 139–146 (2013)
Popescu, F., Blankertz, B., Müller, K.: Computational challenges for noninvasive brain computer interfaces. IEEE Intell. Syst. 23, 78–79 (2008)
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K., Kunzmann, V., Losch, F., Curio, G.: The Berlin Brain-Computer Interface: EEG-based communication without subject training. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 147–152 (2006)
Niazi, I.K., Jiang, N., Jochumsen, M., Nielsen, J.F., Dremstrup, K., Farina, D.: Detection of movement-related cortical potentials based on subject-independent training. Med. Biol. Eng. Comput. 51, 507–512 (2013)
Nascimento, O.F., Nielsen, K.D., Voigt, M.: Movement-related parameters modulate cortical activity during imaginary isometric plantar-flexions. Exp. Brain Res. 171, 78–90 (2006)
Xu, R., Jiang, N., Lin, C., Mrachacz-Kersting, N., Dremstrup, K., Farina, D.: Enhanced Low-latency Detection of Motor Intention from EEG for Closed-loop Brain-Computer Interface Applications. IEEE Trans. Biomed. Eng. 61, 288–296 (2013)
Niazi, I.K., Kersting, N.M., Jiang, N., Dremstrup, K., Farina, D.: Peripheral Electrical Stimulation Triggered by Self-Paced Detection of Motor Intention Enhances Motor Evoked Potentials. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 595–604 (2012)
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Akmal, M. et al. (2016). Universal Matched-Filter Template Versus Individualized Template for Single Trial Detection of Movement Intentions of Different Tasks. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_27
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