Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

Xu, Baoguo; Peng, Si Lun; Song, Aiguo; Yang, Renhuan; Pan, Lizheng

doi:10.5772/45703

Cited by 34 publications

(23 citation statements)

References 28 publications

(28 reference statements)

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“…As shown in Figure 6a, Figure 6c (III). The amplitude ratio a1/a2 for a cycle T/2 could be represented as Equation (14), the logarithmic decrement δ is derived as Equation (15) from Equations (13) and (14). In this paper, δ (i = 3) are calculated in Equation (15).…”

Section: Methodsmentioning

confidence: 99%

“…Elements and actuators that are used in the devices have a profound effect on the compliance and lightness of the whole device. These include springs, pneumatic artificial muscles (PAMs) [1][2][3][4][5][6][7][8][9][10][11], magnetorheological (MR) dampers [12][13][14], shape memory alloys (SMAs) [15][16][17], soft pneumatic actuators (SPAs) [18], and dielectric elastomer actuators (DEAs) [19]. Research and development of these components and devices that employ them are widely conducted.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Effect of a Mechanism Combined with a Speed-Increasing Gear and a Pneumatic Artificial Muscle

Sekine

Kokubun

2018

Actuators

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Abstract:The lightness and softness of pneumatic artificial muscles (PAMs) contribute to their safe use in mechanical devices involved with humans. However, a PAM has limited range of motion (ROM) and a stroke-dependent output force. In this paper, a mechanism combined with a PAM and a speed-increasing gear was developed to improve the tradeoff relationship between the ROM and output force and to verify its benefits in order to enhance the convenience of using PAMs. The gear enhanced the ROM and back-drivability of the PAM, which is beneficial for device safety in daily use. We first designed a mechanism consisting of an antagonistic system-driven PAM and the gear, and then simulated the relationship between the ROM and output force of the mechanism. The effectiveness of the mechanism including the gear was compared with a non-gear mechanism with multiple PAMs. We prototyped the PAM mechanism with and without the gear, and their ROMs, impact absorption, and viscoelasticity were experimentally investigated. Results showed that the gear effectively improved both ROM and output torque below a certain load; moreover, the gear ratio and air pressure had large effects on the external static and dynamic forces, respectively. We confirmed comprehensively the effect and feasibility of the mechanism.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Effect of a Mechanism Combined with a Speed-Increasing Gear and a Pneumatic Artificial Muscle

Sekine

Kokubun

2018

Actuators

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“…The studies have also shown the existence of brain activity on the motor area recorded by electrodes positioned on the central areas of the cortex (C3, Cz, C4). When the subject performs an upper limb Motor Imagery (MI), the considered waves in the contra-lateral electrode concentrates more energy, while in the same side the energy is suppressed [7]- [10]. Others studies aiming at a better EEG resolution adopted a multi-electrode acquisition setup [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Motor imagery recognition and its cerebral mapping

Castro

Masiero

Rocha

et al. 2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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This paper proposes to relate a Linear Discriminant Analysis Classification based system with Quality Similarity Clustering process and a Brain Mapping Technique in order to clarify the brain activity differences that could sustain the better classification rates between each of the Motor Imagery (MI -flex right arm, flex left arm, close right hand, close left hand). To achieve this goal the Eletroencephalogram (EEG) entropy was used as principal feature and a Principal Component Analyses (PCA) was also used. EEG signal, from each of the 8channel transversal set up, was acquired from electrodes Fz, Cz, Pz and Oz to electrodes F3, F4, C3, C4, P3, P4, O1, and O2. Results showed that for each MI there is an specific topographic distribution of the cortex activation. A common point was the minor activation of the motor area. The principal areas were the frontal and the parietal ones. The clustering analysis showed, considering 20% of similarity, that the points which possibly contribute to motor imagery recognition came from those areas. Furthermore, the best results in the classification process were reached to distinguish between left arm and left hand with 89.74% and between arms and hands with 80.77%. Both of these scenes were possible using only the information provided by the frontal electrodes, and this agree with brain mapping. The activation of frontal and parietal lobes reflects the network involved in sensory-motor integration (posterior parietal), decision-making (prefrontal) and preparation of motor response (premotor). Our finding that the frontal electrodes were the most representative may be related to the fact that the premotor cortex is responsible for motor planning and thus, should be the most active area in distinguishing between different motor movements.

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“…No load is applied to the subjects' elbow. The desired trajectories are the same as in (18) and (19). The stimulating frequency and intensity of FES were fixed to 50 Hz and 20 mA, respectively.…”

Section: A Joint Movement Experiments Using Fesmentioning

confidence: 99%