OBJECTIVE: We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). APPROACH: By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. MAIN RESULTS: Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. SIGNIFICANCE: The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.
OBJECTIVE: We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). APPROACH: By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. MAIN RESULTS: Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. SIGNIFICANCE: The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.
Authors: Jane E Huggins; Christoph Guger; Erik Aarnoutse; Brendan Allison; Charles W Anderson; Steven Bedrick; Walter Besio; Ricardo Chavarriaga; Jennifer L Collinger; An H Do; Christian Herff; Matthias Hohmann; Michelle Kinsella; Kyuhwa Lee; Fabien Lotte; Gernot Müller-Putz; Anton Nijholt; Elmar Pels; Betts Peters; Felix Putze; Rüdiger Rupp; Gerwin Schalk; Stephanie Scott; Michael Tangermann; Paul Tubig; Thorsten Zander Journal: Brain Comput Interfaces (Abingdon) Date: 2019-12-10
Authors: Eduardo López-Larraz; Fernando Trincado-Alonso; Vijaykumar Rajasekaran; Soraya Pérez-Nombela; Antonio J Del-Ama; Joan Aranda; Javier Minguez; Angel Gil-Agudo; Luis Montesano Journal: Front Neurosci Date: 2016-08-03 Impact factor: 4.677
Authors: Gelu Onose; Vladimir Cârdei; Ştefan T Crăciunoiu; Valeriu Avramescu; Ioan Opriş; Mikhail A Lebedev; Marian Vladimir Constantinescu Journal: Front Neurosci Date: 2016-09-29 Impact factor: 4.677