Robotic Arm Control with Brain Computer Interface using P300 and SSVEP

Çağlayan, Ozan; Arslan, Reis Burak

doi:10.2316/p.2013.791-082

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Combinations of two or more paradigms have also been proposed to control a robotic arm such as P300/SSVEP [52,53], SSVEP/mVEP [54], SSVEP/MI/Electromyography (EMG) [55], SSVEP/Facial gestures [56], control of a robotic arm and a wheelchair by SSVEP/cervical movements [57], SSVEP/EOG [58], SSVEP/Eye [59], SSVEP/Computer Vision [60], SSVEP/MI [55], and SSVEP/P300/MI [61].…”

Section: Introductionmentioning

confidence: 99%

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Quiles

Dadone

Chio

et al. 2022

Sensors

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Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

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Section: Introductionmentioning

confidence: 99%

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Quiles

Dadone

Chio

et al. 2022

Sensors

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“…The detection of the frequency information can be above 40 Hz, which is not capable in EEG recordings [1,2]. BMI architecture to be designed: 1) should adapt to non-stationary brain dynamics, 2) should generate neural signals from general brain states independently of the users, 3) should have a generalizable structure that will allow an easy transition to control applications, and 4) should demonstrate high classification performance [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Design of MEG-Based Brain-Machine Interface Control Methodology Through Time-Varying Cortical Neural Connectivity & Extreme Learning Machine

Uyulan

2022

Preprint

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Human-machine interfaces contribute to the improvement of the life quality of physically disabled users. In this study, a non-invasive brain-machine interface (BMI) design methodology was proposed to control a robot arm through magnetoencephalography (MEG) based on directionally modulated MEG activity that was acquired during the user's imagined wrist movements in four various directions. The partial directed coherence (PDC) measure derived from functional connectivity between cortical brain regions was utilized in the feature extraction process. The time-varying parameters were estimated based on a time-varying multivariate adaptive autoregressive (AAR) model, that can detect task-dependent features and non-symmetric channel relevance for mental task discrimination. An extreme learning machine (ELM), that utilizes Moore-Penrose (MP) generalized inverse to set its weights and does not necessitate a gradient-based backpropagation algorithm was employed to generate a model with the extracted feature set. The output of the task classification model was embedded into the robotic arm model for realizing control-based tasks. The classification results dictate that the proposed BMI methodology is a feasible solution for rehabilitation or assistance systems that are devised to help motor-impaired people. The proposed methodology provides very satisfactory classification performance at a fast learning speed.

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Robotic Arm Control with Brain Computer Interface using P300 and SSVEP

Cited by 2 publications

References 17 publications

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

Design of MEG-Based Brain-Machine Interface Control Methodology Through Time-Varying Cortical Neural Connectivity & Extreme Learning Machine

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