Brain computer interface for human-cobot interaction in industrial applications

Dmytriyev, Yevheniy; Zaki, Ahmed Magdy Ahmed; Carnevale, Marco; Insero, Federico; Giberti, Hermes

doi:10.1109/hora52670.2021.9461383

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The utilised equipment contains an industrial robot, a 2D industrial camera, an electrical nut runner, and an electromagnetic gripping system. The control of the above equipment was achieved by programming on TM flow v1.82 software [ 37 ], while the detection method was realised through Python v3.8 and MATLAB v9.1 programming on an equipped workstation.…”

Section: Methodsmentioning

confidence: 99%

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

et al. 2023

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For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.

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Section: Methodsmentioning

confidence: 99%

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

et al. 2023

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“…Since this century, however, BCIs have also become a topic in other research areas [78], and thus have applications in other domains as well. For example, as an interface for an augmented reality-based inspection system [79], or as a tool for human robot interaction [80]. Also, for example, to control robots via a BCI [81].…”

Section: B Eeg Based Bci Applicationsmentioning

confidence: 99%

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Penava,

Buettner

2023

IEEE Access

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We demonstrate how to use generative adversarial networks to improve the small data problem when training brain-computer-interfaces. The new approach is based on finely graded frequency bands, which are extracted from motor imagery electroencephalography data by using power spectral density method to synthetically generate electroencephalography data using generative adversarial networks. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets, by first checking the synthetic and real data for statistical and visual similarity, and secondly, by training a random forest classifier, once using only the real data and then using the real data augmented with the synthetic data. With similarity scores of 95.72 % in the subject-dependent case and 83.51 % in the subjectindependent case, and a predictive gain of 17.53 % in the subject-dependent case, and 7.51 % in the subjectindependent case, we were able to achieve promising results. The results show that our approach can make it possible to research rare diseases for which there is too little patient data. Also, synthetic data can be a way for many electroencephalography-based brain-computer interface applications to obtain the required data more cost-and time-efficiently.

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“…Yevheniy Dmytriyev et al [48] developed a task-based algorithm that allowed fluid interaction using BCI bio-signals as a developing collaborative workspaces. Their purpose was to provide a technique for implementing BCI devices in manufacturing contexts so that co-bots may be used and collaborated with easily.…”

Section: Ssvepmentioning

confidence: 99%

Augmented reality and brain-computer interfaces for human-robot collaboration in manufacturing contexts

Gao

Xu²

2022

International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022)

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The most practical methods to allow manipulators to work together in the same workspace and collaborate with humans on the same tasks are brain-computer interfaces (BCIs) and augmented reality (AR). This paper provides a theoretical introduction to AR and BCI technologies in human-robot collaboration (HRC), fully demonstrating the practicability of AR-HRC and BCI-HRC solutions in manufacturing workspaces, including key advantages and future interests. Results show that these solutions have great potential to improve the current manufacture situation and impact the future research of HRC.

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Brain computer interface for human-cobot interaction in industrial applications

Cited by 8 publications

References 10 publications

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

Augmented reality and brain-computer interfaces for human-robot collaboration in manufacturing contexts

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