Approach to Assistive Robotics Based on an EEG Sensor and a 6-DoF Robotic Arm

Cáceres, Camilo; Rosário, João Maurício; Amaya, Darío

doi:10.15866/ireme.v10i4.9323

Cited by 3 publications

(5 citation statements)

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“…Different studies in robotic application obtained acquisition signals from the occipital/parietal brain areas. There are many interfaces used for acquisition signal such as Enobio (Neuroelectrics) with 32 electrodes [40], Epoc Headset (Emotiv) [51,101,137], Ultracortex (OpenBCI) [45,47], Biosemi [103], Neuracle [46,48], BioRadio (Great Lakes NeuroTechnologies) [39], DSI (Wearable sensing) [42], Mindo 4S (National Chiao Tung University Brain Research Center) [43,138], EEG amplifier Nuamps Express (Neuroscan) [44] and NeuSen W8 (Neuracle) [41]. See Table 8 for a summary.…”

Section: Discussionmentioning

confidence: 99%

“…The references reviewed used a combination of frequencies in the low and mediumrange [46][47][48]50,103,137] or in the low range [51,101], while the present study used frequencies in the medium range. This study used a black/white stimulus color as in [51], but with different frequencies, while other studies used stimulus color such as red, blue, and purple, or they used other types of stimuli such as images or names.…”

Section: Discussionmentioning

confidence: 99%

“…Stimulus generation for the elicitation of the SSVEP can be based on light-emitting diodes LEDs [114] or monitors [46,51,101,103,108,115]. In this study, intermittent visual stimuli are presented on the computer screen.…”

Section: Stimulus Generationmentioning

confidence: 99%

“…With these design specifications, the SSVEP paradigm has been selected, which, compared to the other paradigms commonly used in EEG BCIs, provides greater communication speed [7,10,97,98], the subject requires less training time [10,55,[97][98][99], and can be operated with fewer electrodes placed on the occipital region [46,48,50,51,[100][101][102][103]. The SSVEP signal appears in the visual cortex when the subject observes intermittent stimuli [104], and its response depends on the subject's attention [105][106][107] and the size, shape, and frequency of the stimulus [7,19,108].…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Stimulus Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Quiles

Dadone

Chio

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…( 2018), um modelo do gêmeo digital precisa ser desenvolvido com um, produto em todas as fases do seu processo de desenvolvimento.…”

Section: Acspunclassified

Modelo de referência do gêmeo digital no ciclo de vida do produto.

Facchini

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Ao longo da trajetória deste trabalho recebi apoio e ajuda de muitas pessoas que formaram minha base. Portanto, seguem aqui algumas palavras de gratidão que simbolizam meu muito obrigada e minha gratidão por esta conquista.Primeiramente, agradeço a Deus, pelo dom da vida e por sempre estar presente nas minhas conquistas.Ao Professor Dr. Eduardo de Senzi Zancul, pela confiança, orientação, conselhos e por ser um mentor ao longo de todos esses anos. Aos Professores Dr.-Ing. Klaus Schützer e Dr. André Leme Fleury, pelas colaborações em diferentes momentos durante minha trajetória acadêmica.A Willian Kenji Gushiken, representante da empresa integral PLM, que disponibilizou seu produto para aplicação e estudo, além de nos apoiar com as dúvidas de como utilizar o software.À minha família, amigos e ao grupo de pesquisa, por me apoiaram e acreditarem em mim. Agradeço, em especial, minha mãe, Maria Dorotilde, por sempre me apoiar profissionalmente e me encorajar a me tornar o que eu sonhava ser, independente de qual carreira eu escolhesse.Ao meu marido, Humberto Facchini, aquele que sempre me apoiou, compreendeu e ajudou, durante os muitos anos dedicados a esta conquista.Com amor, admiração e gratidão, dedico este trabalho a Deus, à minha família e amigos, pela compreensão, carinho, presença e incansável apoio, ao longo do período de elaboração do trabalho.

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