Balancing a simulated inverted pendulum through motor imagery: An EEG-based real-time control paradigm

Yue, Jingwei; Zhou, Zongtan; Jiang, Jun; Liu, Yadong; Hu, Dewen

doi:10.1016/j.neulet.2012.07.031

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…Moreover, such a mechanical system can be applied in various fields. Namely, the model of inverted pendulum (especially, under various kinds of control of the suspension point motion) is widely used in physics [32], applied mathematics [38], engineering science [21,31,33], neuroscience [39], economics [30], etc.…”

Section: Introductionmentioning

confidence: 99%

Elastic inverted pendulum with backlash in suspension: stabilization problem

2015

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In this paper, we investigate the elastic inverted pendulum with hysteretic nonlinearity (a backlash) in the suspension point. Namely, the problems of stabilization and optimization of such a system are considered. The algorithm (based on the bionic model) which provides the effective procedure for finding of optimal parameters is presented and applied to considered system. The results of numerical simulations, namely the phase portraits and the dynamics of Lyapunov function, are also presented and discussed.

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Section: Introductionmentioning

confidence: 99%

Elastic inverted pendulum with backlash in suspension: stabilization problem

2015

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“…Commonly used EEG signals include the event-related P300 potentials [14][15][16][17][18], steady-state visual evoked potentials (SSVEPs) [19][20][21][22][23], and the motor imagery (MI)-related rhythms [24][25][26][27][28][29][30]. In these, the P300 potentials and SSVEPs are evoked by external stimuli and the MI rhythms are voluntarily modulated by the subjects.…”

Section: Introductionmentioning

confidence: 99%

“…The corresponding distinguishable features in the EEG signals can be used to design MI-based BCI systems [24][25][26][27][28][29][30]. Many BCI studies have reported good offline results with high accuracies.…”

Section: Introductionmentioning

confidence: 99%

Real-time brain computer interface using imaginary movements

El-Madani

Sørensen

Kjær

et al. 2015

EPJ Nonlinear Biomed Phys

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Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods: Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results: Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions:The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies.

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“…While utilization of the spatially separable movement-related activities (e.g., right vs left hand movements) is well mastered in the field of BCI (see e.g., [3]), discrimination of different types of movements of the same limb is known as a complex and challenging task since all these movements activate roughly the same area of the cortex [4]. Due to this, temporal and frequency characteristics of the EEG have to be used to classify the movement type.…”

Section: Introductionmentioning

confidence: 99%

Subband optimization for EEG-based classification of movements of the same limb

Dobias

Šťastný

2014

2014 International Conference on Applied Electronics

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The contribution investigates the impact of frequency feature optimization on discriminating between movement-related EEG realisations associated with right shoulder elevation and right index finger flexion movements. Exhaustive search of subbands in the range from 5 to 45 Hz is performed. A classifier based on Hidden Markov Models is utilised. The results show a large variability of optimal settings among subjects and electrodes. Using subband optimization an average 3.5% increase in classification accuracy of EEG filtered using 8-neighbor Laplacian filter was achieved, reaching an overall score of 81.2±1.2%, individual improvements ranging from 1.2 to 9.9%. The best general setting common for all subject was confirmed as 5-40 Hz.

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Balancing a simulated inverted pendulum through motor imagery: An EEG-based real-time control paradigm

Cited by 20 publications

References 17 publications

Elastic inverted pendulum with backlash in suspension: stabilization problem

Elastic inverted pendulum with backlash in suspension: stabilization problem

Real-time brain computer interface using imaginary movements

Subband optimization for EEG-based classification of movements of the same limb

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