A lower limb exoskeleton control system based on steady state visual evoked potentials
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, si...
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| Published in | Journal of neural engineering Vol. 12; no. 5; pp. 56009 - 56022 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
IOP Publishing
01.10.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1741-2560 1741-2552 1741-2552 |
| DOI | 10.1088/1741-2560/12/5/056009 |
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| Abstract | 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. |
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| AbstractList | 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 plus or minus 5.73%, a response time of 3.28 plus or minus 1.82 s, an information transfer rate of 32.9 plus or minus 9.13 bits/min, and a completion time of 1100 plus or minus 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. We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). 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. 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. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible. We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs).OBJECTIVEWe have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs).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.APPROACHBy 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.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.MAIN RESULTSOverall, 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.The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.SIGNIFICANCEThe 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. |
| Author | Müller, Klaus-Robert Kwak, No-Sang Lee, Seong-Whan |
| Author_xml | – sequence: 1 givenname: No-Sang surname: Kwak fullname: Kwak, No-Sang email: nskwak@korea.ac.kr organization: Department of Brain and Cognitive Engineering, Korea University , Anam-dong, Seongbuk-ku, Seoul, Korea – sequence: 2 givenname: Klaus-Robert surname: Müller fullname: Müller, Klaus-Robert email: klaus-robert.mueller@tu-berlin.de organization: Department of Computer Science Machine Learning Group, TU Berlin, Berlin, Germany – sequence: 3 givenname: Seong-Whan surname: Lee fullname: Lee, Seong-Whan organization: Department of Brain and Cognitive Engineering, Korea University , Anam-dong, Seongbuk-ku, Seoul, Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26291321$$D View this record in MEDLINE/PubMed |
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| Snippet | Objective. We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked... We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials... |
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| StartPage | 56009 |
| SubjectTerms | Adult Artificial Limbs Brain-Computer Interfaces brain-machine interface Classification Control systems electroencephalogram Electroencephalography - instrumentation Equipment Design Equipment Failure Analysis Evoked potentials Evoked Potentials, Visual exoskeleton control Exoskeleton Device Exoskeletons Extraction Feedback Gait Disorders, Neurologic - physiopathology Gait Disorders, Neurologic - rehabilitation Humans Limbs Lower Extremity Male Robotics - instrumentation steady state visual evoked potentials Stimulation Visual Visual Perception |
| Title | A lower limb exoskeleton control system based on steady state visual evoked potentials |
| URI | https://iopscience.iop.org/article/10.1088/1741-2560/12/5/056009 https://www.ncbi.nlm.nih.gov/pubmed/26291321 https://www.proquest.com/docview/1716253501 https://www.proquest.com/docview/1825451483 |
| Volume | 12 |
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