EEG-Based Detection of Starting and Stopping During Gait Cycle

Hortal, Enrique; Úbeda, Andrés; Íáñez, Eduardo; Azorín, José M.; Fernández, Eduardo

doi:10.1142/s0129065716500295

Cited by 38 publications

(40 citation statements)

References 27 publications

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“…MRCP corresponding to self-paced movement is known as Bereitschafts Potential (BP), and it is characterized by a slow decrease in EEG amplitude over the primary motor cortex within at least 0.5 s preceding the movement initiation. On the other hand, ERD is defined as a decrease in spectral power 0.5-2 s before movement onset reported most in the mu (8)(9)(10)(11)(12) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands of the brain wave [16][17][18]. The limits of the frequency bands may differ across different authors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gait intention from pre-movement EEG signals: a feasibility study

Hasan

Siddiquee

Atri

et al. 2020

J NeuroEngineering Rehabil

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Background: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a tenfold cross-validation scheme. Results: Using a fully predictive intention detection system, 76.41 ± 4.47% accuracy, 72.85 ± 7.48% sensitivity, and 79.93 ± 5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12 ± 4.12%, 70.24 ± 6.45%, and 77.78 ± 7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06 ± 8.27% with 1.45 False Positives/min. Conclusion: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

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Section: Introductionmentioning

confidence: 99%

Prediction of gait intention from pre-movement EEG signals: a feasibility study

Hasan

Siddiquee

Atri

et al. 2020

J NeuroEngineering Rehabil

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“…Otros filtros de interés son los filtros espaciales que se basan en eliminan la influencia de los otros electrodos, como el filtro Laplaciano o el de referencia común media (CAR: Common Average Reference) [17].…”

Section: Preprocesamientounclassified

“…• Potenciales evocados: Las características extraídas de la señal de EEG en este tipo de BMI son aquellas producidas automáti-camente por el cerebro como consecuencia de un estímulo externo [17]. Uno de los más conocidos potenciales evocados es el P300.…”

Section: Selección De Característicasunclassified

Nueva aproximación para rehabilitar la marcha basada en la combinación de neurotecnologías y exoesqueletos

Rodríguez-Ugarte

2018

DOCTUMH

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Cada día se está más cerca de conseguir que personas que han sufrido un accidente cerebrovascular (ACV) vuelvan a caminar con menos dificultad. Para mejorar la rehabilitación y el incremento de la plasticidad cerebral (LTP) en personas que han sufrido un ACV, nuestro grupo propone la combinación de técnicas de neuroestimulación (tDCS/tACS), interfaces cerebro-máquina (BMIs) y exoesqueletos. La neuroestimulación tDCS/tACS es una técnica relativamente nueva que está siendo ampliamente investigada a día de hoy para comprobar si produce plasticidad cerebral. Aunque se ha investigado mucho las BMI en las últimas décadas, lograr una BMI precisa y efectiva es algo arduo y tedioso en el campo de la rehabilitación o control de extremidades (e.g. exoesqueletos). Los exoesqueletos son órtesis colocadas en alguna extremidad del cuerpo que permiten asistir durante el movimiento. Este artículo describe las tecnologías que se van a usar para mejorar las terapias de rehabilitación actuales. Además, se explican las diferentes fases del proyecto ASSOCIATE para lograr nuestro objetivo.

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“…Through the analysis of the above clinical researches, it is found that the rehabilitation effect of exoskeleton robot varies in different stages of stroke and different joints of limbs [13][14][15][16] , the key to improving its clinical effect lies in the mechanism design and software control [17][18][19][20] , and there is no mature research paradigm of gait training assisted by exoskeleton robot for stroke patients [21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

Clinical research of lower extremity exoskeleton robot In post-stroke hemiplegic patients

Wang¹,

Yu²,

Hui-min³

et al. 2020

Preprint

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Background In recent years, the number of people suffering from stroke-induced motor function lost increased considerably. Recovery of motility is essential in improving their quality of life. However, the existing rehabilitation methods cannot fulfill patients’ training requirements. As a new rehabilitation technology, exoskeleton robot provides a new treatment scheme for post-stroke hemiplegic patients. Objective To explore the safety and effectiveness of exoskeleton-assisted gait training for post-stroke hemiplegic patients. Methods A lower extremity hip joint exoskeleton robot was designed by the cooperation team, and 9 post-stroke hemiplegic patients were included for exoskeleton-assisted gait training. The three-dimensional gait parameters, plantar pressure and surface electromyography were used to validate the effectiveness of the exoskeleton robot in post-stroke hemiplegic patients. Results Exoskeleton-assisted ambulation training for post-stroke hemiplegic patients can correct the asymmetry of gait and abnormal phase of gait cycle, increase the toe-ground clearance and the angle of hip joint, reduce the pressure of non-paretic plantar and the impulse of bilateral feet, which help balance the plantar pressure distribution. It can also improve the integral electromyography value of specific muscle, which means that corresponding muscles are stimulated to generate activities. Conclusions The exoskeleton robot designed by our team can correct the hemiplegic gait and foot drop phenomenon of post-stroke patients, protecting them from damage caused by long-term abnormal gait, which contributes to the recovery of lower extremity motor function in hemiplegic patients. The results also provide important information for the design of lower extremity exoskeleton robot. In the future, we should further explore the rehabilitation function of exoskeleton robot in post-stroke hemiplegic patients, as well as the ambulation and rehabilitation function in other lower extremity motor dysfunction patients.

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EEG-Based Detection of Starting and Stopping During Gait Cycle

Cited by 38 publications

References 27 publications

Prediction of gait intention from pre-movement EEG signals: a feasibility study

Prediction of gait intention from pre-movement EEG signals: a feasibility study

Nueva aproximación para rehabilitar la marcha basada en la combinación de neurotecnologías y exoesqueletos

Clinical research of lower extremity exoskeleton robot In post-stroke hemiplegic patients

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