Controlling a Robotic Arm by Brainwaves and Eye Movement

Postelnicu, Cristian-Cezar; Talabă, Doru; Toma, Madalina-Ioana

doi:10.1007/978-3-642-19170-1_17

Cited by 23 publications

(8 citation statements)

References 11 publications

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“…Our present case of EEG peak in response to a voluntary change of horizontal eye gaze direction might be useful for patients with locked-in syndrome or other disabilities for controlling the direction of computer cursor in BCI applications [32]. This approach might also be translatable for EEG-based command of the movement of a robotic arm or wheelchair in HMI applications [30,33].…”

Section: Discussionmentioning

confidence: 92%

Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

et al. 2017

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The optimization of peak detection algorithms for electroencephalogram (EEG) signal analysis is an ongoing project; previously existing algorithms have been used with different models to detect EEG peaks in various applications. However, none of the existing techniques perform adequately in eye event-related applications. Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, Liu and Dingle peak detection models, where the associated features are considered as inputs to the NNRW with and without FWL. The combination of all the associated features from the four models is also considered, as a comprehensive model for validation purposes. Real EEG data recorded from two channels of 20 healthy volunteers were used to perform the model simulations. The data set consisted of 40 peaks arising in the frontal eye field in association with a change of horizontal eye gaze direction. It was found that the NNRW in conjunction with FWL has better performance than NNRW alone for all four peak detection models, of which the Dingle model gave the highest performance, with 74% accuracy.

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Section: Discussionmentioning

confidence: 92%

Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

et al. 2017

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“…The combination of EOG and EMG has also been applied in the control of a robot [18]. In [19,20], the multimodality is used to improve the behavior of an EOG interface (in combination with EEG signals) or a BCI (combined with MEG signals) respectively. EEG was also combined with eye tracking to control a quadcopter [21].…”

Section: Introductionmentioning

confidence: 99%

Combining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm

Hortal

Íáñez

Úbeda

et al. 2015

Robotics and Autonomous Systems

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“…The electrode sensors that are placed on the surface of the brain are more relevant than the electrode sensors that are placed around the eyes [12]. Based on a literature study, a number of EOG eye blink signals have widely been proposed to control cursor movement [34], a robotic arm [35], and wheelchair [9]. However, study's on the EEG eye blink signals are still limited.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different Peak Models of Eye Blink Eeg for Signal Peak Detection Using Artificial Neural Network

Adam¹,

Ibrahim²,

Mokhtar³

et al. 2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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Controlling a Robotic Arm by Brainwaves and Eye Movement

Cited by 23 publications

References 11 publications

Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

Combining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm

Evaluation of Different Peak Models of Eye Blink Eeg for Signal Peak Detection Using Artificial Neural Network

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