http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Ramos Murguialday, A. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation
Vidaurre, C.,Ramos Murguialday, A.,Haufe, S.,Gó,mez, M.,Mü,ller, K.-R.,Nikulin, V.V. ACADEMIC PRESS 2019 NEUROIMAGE Vol.199 No.-
<P><B>Abstract</B></P> <P>An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Afferent stimulation (STM) in the calibration phase was used to enhance BCI performance. </LI> <LI> Concurrent motor imagery and STM had stronger modulation of sensorimotor oscillations. </LI> <LI> STM significantly improved BCI accuracy particularly for poorly performing subjects. </LI> <LI> Classifiers trained with STM can be successfully used online even without stimulation. </LI> <LI> These findings ease the practical applicability of STM-based BCI systems. </LI> </UL> </P>
-
EEG-based BCI for the linear control of an upper-limb neuroprosthesis
Vidaurre, C.,Klauer, C.,Schauer, T.,Ramos-Murguialday, A.,Muller, K.R. Butterworth-Heinemann 2016 Medical engineering & physics Vol.38 No.11
Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI-FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
