An EEG-based robot arm control to express human emotions

Ogino, Mikito; Mitsukura, Yasue

doi:10.1109/amc.2019.8371111

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Especially in recent several years, researchers focused more on optimization of technology. Ogino and Mitsukura developed an emotion analyzer which could be used in a robotic arm system [ 33 ]. Korovesis et al presented a system using alpha brain waveforms to get a synchronous and endogenous EEG-BCI which could help control a mobile robot with the eye's blinking of the subject [ 34 ].…”

Section: Recent Development Of Noninvasive Methods For Robotic Contro...mentioning

confidence: 99%

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future

Liu

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs “Mapping Knowledge Domains” (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.

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Section: Recent Development Of Noninvasive Methods For Robotic Contro...mentioning

confidence: 99%

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future

Liu

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…The findings of Paul Ekman-a psychologist at the University of California, San Francisco-indicate that human emotions comprise several core emotions, similar to the three primary colors (red, yellow, and blue) [1]. In modern times, emotion recognition has gradually come to be known to a wider public and plays an important role in different fields, e.g., rapid diagnosis by monitoring patients' emotions in the medical industry [2], robots imitating human emotions [3], and image retrieval using emotions [4].…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Emotion Recognition Based on Domain Similarity of EEG Signal Transfer Learning

Zhao

Meng

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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For solving the problem of the inevitable decline in the accuracy of cross-subject emotion recognition via Electroencephalograph (EEG) signal transfer learning due to the negative transfer of data in the source domain, this paper offers a new method to dynamically select the data suitable for transfer learning and eliminate the data that may lead to negative transfer. The method which is called cross-subject source domain selection (CSDS) consists of the next three parts. 1) First, a Frankcopula model is established according to Copula function theory to study the correlation between the source domain and the target domain, which is described by the Kendall correlation coefficient. 2) The calculation method for the Maximum Mean Discrepancy is improved to determine the distance between classes in a single source. After normalization, the Kendall correlation coefficient is superimposed, and the threshold is set to identify the source-domain data most suitable for transfer learning. 3) In the process of transfer learning, on the basis of Manifold Embedded Distribution Alignment, the Local Tangent Space Alignment method is used to provide a low-dimensional linear estimation of the local geometry of nonlinear manifolds, which maintains the local characteristics of the sample data after dimensionality reduction. Experimental results show that compared with the traditional methods, the CSDS increases the accuracy of emotion classification by approximately 2.8% and reduces the runtime by approximately 65%.

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“…While EEG devices have been previously used in the robotics field for robot's motor control (Alimardani et al, 2013 ; Staffa et al, 2019 ), very few approaches exist for the classification of a human's internal state during human-robot interaction (Alimardani and Hiraki, 2020 ; Staffa and Rossi, 2022 ; Staffa and D'Errico, 2023 ). For instance, Ogino and Mitsukura ( 2018 ) used a machine learning algorithm to identify user emotions from EEG signals when interacting with a robot. Another study Toichoa Eyam et al ( 2021 ) used an EEG-based emotion sensing system to control a robot to perform different actions based on the user's emotions and showed that such an approach can improve human-robot interaction.…”

Section: Introductionmentioning

confidence: 99%

Classifying human emotions in HRI: applying global optimization model to EEG brain signals

Staffa,

D'Errico,

Sansalone

et al. 2023

Front. Neurorobot.

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Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future.

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An EEG-based robot arm control to express human emotions

Cited by 9 publications

References 18 publications

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future

Cross-Subject Emotion Recognition Based on Domain Similarity of EEG Signal Transfer Learning

Classifying human emotions in HRI: applying global optimization model to EEG brain signals

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