A New Hybrid Motion Planner: Applied in a Brain-Actuated Robotic Wheelchair

Lopes, Ana Cristina Macário; Rodrigues, João M. O. S.; Perdigão, Jorge; Pires, Gabriel; Nunes, Urbano

doi:10.1109/mra.2016.2605403

Cited by 43 publications

(30 citation statements)

References 22 publications

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“…Thus, a P300 potential is elicited whenever the letter the user is paying attention to flashes, and so the target letter can be identified by a P300 detection algorithm and then transmitted. The use-cases of P300based BCIs have greatly increased over the past years, from steering a wheelchair (Lopes et al, 2016) to composing music (Pinegger et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

et al. 2020

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There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.

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Section: Introductionmentioning

confidence: 99%

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

et al. 2020

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“…Additionally, inertia is considered via parameter to suppress unintended sudden movement of the robotic arm. Similar BMI studies of shared control also considered the issue of reducing acceleration, applying a smoothing approach to motion planning [31]. At Eq.…”

Section: Methodsmentioning

confidence: 99%

Vision-aided brain–machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury

et al. 2019

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BackgroundWhile spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain–machine interface training system for robotic arm control is proposed and developed.MethodsThe proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects.ResultsIn a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p < 0.0001). Functional magnetic resonance imaging after five sessions of observation-based training with the developed system showed brain activation patterns with tendency of focusing to ipsilateral primary motor and sensory cortex, posterior parietal cortex, and contralateral cerebellum. However, shared control with blending parameter α less than 1 was not successful and success rate for touching an instructed target was less than the chance level (= 50%).ConclusionsThe pilot clinical study utilizing the training system suggested potential beneficial effects in characterizing the brain activation patterns.

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“…Besides, with the use of a security point, the agent avoids the problem of wrong positions and can perform smooth and safe maneuvers. The work presented by Lopes et al (2016) developed a path planner algorithm based on A*. This approach considers the wheelchair geometry, and it imposes incidence angles in each waypoint.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Methodology for Autonomous Crossing Narrow Passages Applied on Assistive Mobile Robots

Maciel

Pinto

Júnior

et al. 2019

J Control Autom Electr Syst

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The assistive robotics field works with technologies aimed at assisting people with physical impairments. Simple tasks like passing through doorways can be a significant challenge for motored wheelchairs users. Besides, depending on the disability degree, the task may be impossible to complete without help. Therefore, mobile robotics technologies have been implemented in wheelchairs to give them intelligence, and a supervisory interface inserts the user in the control loop to complete the objectives. Thus, this research proposes an innovative method to assist the user to cross narrow passages automatically. The methodology works in known and unknown environments. Besides, it does not require mapping and global localization algorithms. In this technique, a depth camera recognizes the edges between a narrow path. A Kalman filter method is used to improve the precision of these points even when the passage leaves the camera field of view. Finally, a nonlinear controller performs the narrow passage crossing. This methodology was analyzed using a MATLAB model, Gazebo simulation platform, and a real mobile robot Pioneer-P3DX. The results demonstrated promising applicability. Keywords Narrow passages • Assistive robotics • Mobile robots • Collision avoidance • Intelligent wheelchair 1 Introduction Depending on the disability degree, many people are not able to use both manuals and motorized wheelchairs with an independent movement. For instance, people with quadriplegia or with neuromuscular diseases and older people are unable to use wheelchairs with independence. Therefore, in the last years, researches have been using original mobile robotics technologies for the development of intelligent wheelchairs (IWs) to overcome this issue, such as in Simpson (2005). Different from conventional motorized wheelchairs, the IW has good processing capability, sensors, actuator, and communication interfaces with the user. An intelligent wheelchair is a standard power wheelchair containing a sen

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A New Hybrid Motion Planner: Applied in a Brain-Actuated Robotic Wheelchair

Cited by 43 publications

References 22 publications

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

Vision-aided brain–machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury

Methodology for Autonomous Crossing Narrow Passages Applied on Assistive Mobile Robots

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