Development of an EMG based SVM supported control solution for the PlatypOUs education mobile robot using MindRove headset

Noboa, Erick; Rácz, Melinda; Szücs, László; Galambos, Péter; Márton, Gergely; Eigner, György

doi:10.1016/j.ifacol.2021.10.273

Cited by 7 publications

(5 citation statements)

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“…In our signal processing step, we applied a fifth-order bandpass Butterworth filter with a range of 1 to 45 Hz, and utilized the FASTER algorithm [54] to detect and remove artifacts associated with eye movements and muscle activity. This is crucial to ensure that classification is performed on pure EEG signals rather than artifacts, because it has been demonstrated in [55] that electromyography can be successfully used for BCI purposes.…”

Section: Discussionmentioning

confidence: 99%

“…This is attributable to the fact that the amplitude of electromyographic signals can be orders of magnitude greater than EEG signals. Furthermore, it has been demonstrated that artifacts can be successfully utilized for BCI purposes [55]; however, in our view, a genuine BCI should not rely on artifacts but solely on pure EEG signals. In addition, concerning a prominent international BCI competition, the Cybathlon "bionic Olympics" [11], participating BCI teams are required to implement an online artifact removal algorithm.…”

Section: Introductionmentioning

confidence: 93%

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Deep Comparisons of Neural Networks from the EEGNet Family

et al. 2023

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A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one of the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number of subjects, typically less than or equal to 10. Furthermore, the algorithms usually include only bandpass filtering as a means of reducing noise and increasing signal quality. In this study, we conducted a comparative analysis of five renowned neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, and MI-EEGNet) utilizing open-access databases with a larger subject pool in conjunction with the BCI Competition IV 2a dataset to obtain statistically significant results. We employed the FASTER algorithm to eliminate artifacts from the EEG as a signal processing step and explored the potential for transfer learning to enhance classification results on artifact-filtered data. Our objective was to rank the neural networks; hence, in addition to classification accuracy, we introduced two supplementary metrics: accuracy improvement from chance level and the effect of transfer learning. The former is applicable to databases with varying numbers of classes, while the latter can underscore neural networks with robust generalization capabilities. Our metrics indicated that researchers should not disregard Shallow ConvNet and Deep ConvNet as they can outperform later published members of the EEGNet family.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Deep Comparisons of Neural Networks from the EEGNet Family

et al. 2023

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“…They ensure that while a robot remains agile in its movements, it doesn't endanger its integrity or that of its environment. [116], ICP 3 + SVM 4 [132], 2D multi-SLAM [77], GMapping [87], [88], [105], FNN 5 [27], Visual-SLAM + RTAB-Map 6 [126], RTAB-Map [94], Cartographer [65], Monocular SLAM + RL [36], KimeraMulti [133] 2 -Object Detection YOLOv4 7 [62], [103], YOLOv3 [31], [59], [62], YOLOv2 [134], PointNet [135], CNN 8 [21], [23], [136], SVM [137], MobileNet [138], YOLOv2 + JPDA 9 + IMM 10 [139] 3 -Object Tracking…”

Section: Householdmentioning

confidence: 99%

“…PeopleBot [42], [91] Pioneer 3-DX [19], [36], [42], [72], [119] Clearpath Robotics Husky [28], [75], [85], [116], [174], [175], [178] Clearpath Robotics Jackal [18], [28], [53], [94] Willow Garage TurtleBot series [2], [8], [9], [12], [25], [30], [35], [70], [81], [88], [100], [105], [143], [144], [147], [148], [150], [158], [178]- [185] Willow Garage PR2 [87], [124] Roomba vacuum cleaner [86], [172] Eddie [186] Rob@work 3 [187] KUKA YouBot [138], [142] Arlobot [134] PlatypOUs [137] Ceres (Volksbot platform)…”

Section: Mobile Robotsmentioning

confidence: 99%

“…Reference Numbers Gazebo Simulators Gazebo only [13], [15], [18], [22], [28], [30], [33], [35], [36], [53], [78], [84]- [88], [90], [92]- [96], [98], [102], [104]- [107], [111], [112], [115], [116], [123], [125], [131], [137], [140], [143], [144], [147], [150], [151], [155], [156], [158], [159], [162], [166], [178], [181], [188], [192], [193], [195], [197], [199]-[202] Gazebo + RViz [1],…”

Section: Simulator Typementioning

confidence: 99%

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Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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