Abstract
The ability of walking brings us a great freedom in our daily life. However, there is a huge number of people who have this ability diminished or are not even able to walk due to motor disabilities. This paper presents a method to detect the voluntary initiation and stop of the gait cycle using the ERD phenomenon. The system developed obtains a good accuracy in the detection of the rest and walking state (70.5 % and 75.0 %, respectively). Moreover, the average detection of the onset and ending instants of the gait is detected with a 65.2 % of accuracy. Taking into account the number of intentions of initiation and stop of the gait, the system reaches a good True Positive Rate (around 65%) but obtaining a still improvable False Positive Rate (15.4 FP/min in average). By reducing this factor, this detection system can be used in future works to control a lower limb exoskeleton or a wearable robot. These devices are very useful for rehabilitation and assistance procedures in patients with motor problems affecting their lower limb.
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References
Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. The Lancet Neurology 7(11), 1032–1043 (2008)
Wei, L., Yue, H., Jiang, X., He, J.: Brain Activity during Walking in Patient with Spinal Cord Injury. In: International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 96–99 (2011)
Hortal, E., Úbeda, A., Iáñez, E., Azorín, J.M.: Control of a 2 DoF Robot Using a Brain-Machine Interface. Computer Methods and Programs in Biomedicine 116(2), 169–176 (2014), New methods of human-robot interaction in medical practice,
Wolpaw, J.R., Birbaumerc, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)
Moreno, J.C., Collantes, I., Asin, G., Pons, J.L.: Design of better robotic tools adapted to stroke rehabilitation practice. In: World Congress on Medical Physics and Biomedical Engineering (2012)
Bortole, M., del Ama, A.J., Rocon, E., Moreno, J.C., Brunetti, F., Pons, J.L.: A Robotic Exoskeleton for Overground Gait Rehabilitation. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3356–3361 (2013)
Bai, O., et al.: Prediction of human voluntary movement before it occurs. Clinical Neurophysiology 122, 364–372 (2011)
Ibáñez, J., Serrano, J.I., del Castillo, M.D., Barrios, L., Gallego, J.Á., Rocon, E.: An EEG-Based Design for the Online Detection of Movement Intention. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part I. LNCS, vol. 6691, pp. 370–377. Springer, Heidelberg (2011)
Planelles, D., Hortal, E., Costa, A., Iáñez, E., Azorín, J.M.: First steps in the development of an EEG-based system to detect intention of gait initiation. In: 8th Annual IEEE International Systems Conference, Ottawa, Canada, pp. 167–171 (2014)
Dollar, A.M., Herr, H.: Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art. IEEE Transactions on Robotics 24(1), 144–158 (2008)
Moreno, J.C., del Ama, A.J., de los Reyes-Guzmán, A., Gil-Agudo, A., Ceres, R., Pons, J.L.: Neurorobotic and hybrid management of lower limb motor disorders: a review. Medical & Biological Engineering & Computing 49(10), 1119–1130 (2011)
Koralek, A.C., Jin, X., Long, J.D., Costa, R.M., Carmena, J.M.: Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331–335 (2012)
Shibasaki, H., Hallett, M.: What is the Bereitschaftspotential? Clinical Neurophysiology 117, 2341–2356 (2006)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology 110(11), 1842–1857 (1999)
Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification (2003), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Flórez, F., Azorín, J.M., Iáñez, E., Úbeda, A., Fernández, E.: Development of a low-cost SVM-based spontaneous Brain-Computer Interface. In: International Conference on Neural Computation Theory and Applications, pp. 415–421 (2011)
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Hortal, E., Úbeda, A., Iáñez, E., Fernández, E., Azorín, J.M. (2015). Using EEG Signals to Detect the Intention of Walking Initiation and Stop. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_29
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DOI: https://doi.org/10.1007/978-3-319-18914-7_29
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