Development of a P300 Brain–Machine Interface and Design of an Elastic Mechanism for a Rehabilitation Robot

Huang, Han-Pang; Liu, Yi-Hung; Lin, Wei; Kang, Zhi-Hao; Cheng, Ching-An; Huang, Tzung-Chi

doi:10.5875/ausmt.v5i2.518

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Wheeled robots are mainly featured with human-like upper limbs and a lower limb motion system to replace the human leg. 26 Compared with wheeled robots, biped robots have much higher production costs. They are also more difficult to operate.…”

Section: Physical Appearance and Preference For Home Care Robotsmentioning

confidence: 99%

“…Humanoid robots have three features: they perform tasks in the human environment, use the same tools as those used by human beings and have a human‐like appearance. Wheeled robots are mainly featured with human‐like upper limbs and a lower limb motion system to replace the human leg 26 . Compared with wheeled robots, biped robots have much higher production costs.…”

Section: Literature Reviewmentioning

confidence: 99%

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Colour‐emotion association and colour preference for elder care robot appearance

Liao

Hsu

2022

Coloration Technology

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With the population ageing in Taiwan, it is projected that elder care robots incorporating smart technologies will play an essential role in ambient assisted living. This research has two purposes: (1) to investigate whether older adults' colour‐emotion associations and colour preferences for robot appearance affect their perceptual judgements; and (2) to explore gender differences in their judgements. Phase I of this research uses a questionnaire to investigate 91 participants' preferences for robot style and their emotional trigger words for the role of robots. Phase II experiments were performed on another 60 older adults to identify whether their colour‐emotion associations and colour preferences affect their perceptual judgements. The research results show that, regardless of gender difference, participating older adults prefer a robot with non‐human–like features. The results also show that there is no significant difference between males and females in terms of the effect of colour‐emotion association on a robot's appearance. Older adults tend to associate warm colours with emotional semantics, such as friendly, comfortable, reassuring, gentle and lively. Preferred colours include red, white and yellow. Black and grey are almost never preferred by older adults. There are significant differences by gender in the preferences for the colours white and purple. Older females prefer purple more, while white is preferred by older males. For the other colours, there were no significant differences between males and females. Colour attributes do not have any effect on colour‐emotion association, whereas colour preference is highly positively correlated with b*.

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Section: Physical Appearance and Preference For Home Care Robotsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Colour‐emotion association and colour preference for elder care robot appearance

Liao

Hsu

2022

Coloration Technology

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“…An SSVEP-controlled assist robot has been developed to perform pick and place tasks (Chen et al, 2019 ), which are completed by showing the user four targets flickering at 30, 31, 32, 33 Hz to trigger different commands. In the same regard, SSVEPs are used to control functional electrical stimulation for rehabilitation (Han-Pang, 2015 ). This paradigm has good information transfer rate (ITR) and accuracy (Vialatte et al, 2010 ; Chen et al, 2019 ), but is restricted by the limited number of control commands (Norcia et al, 2015 ; Zhao et al, 2015 ), and constant flickering easily causes fatigue, which is not appropriate for people in rehabilitation (Duszyk et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

et al. 2020

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Background and Objective: Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still not enough for online use. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component. Moreover, we have developed an upper-limb assist robot system with the method for verification and hope to really help paralyzed people. Methods: We chose P300, which is highly available and easily accepted to obtain the user's intention. Preprocessing and spatial enhancement were firstly implemented on raw EEG data. Then, three approaches-linear discriminant analysis, support vector machine, and multilayer perceptron-were compared in detail to accomplish an efficient P300 detector, whose output was employed as a command to control the assist robot. Results: The method we proposed achieved an accuracy of 94.43% in the offline test with the data from eight participants. It showed sufficient reliability and robustness with an accuracy of 80.83% and an information transfer rate of 15.42 in the online test. Furthermore, the extended test showed remarkable generalizability of this method that can be used in more complex application scenarios. Conclusion: From the results, we can see that the proposed method has great potential for helping paralyzed people easily control an assist robot to do numbers of things.

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A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

Liu

Wang

2016

Applied Sciences

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This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user's EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher's discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.

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Development of a P300 Brain–Machine Interface and Design of an Elastic Mechanism for a Rehabilitation Robot

Cited by 3 publications

References 19 publications

Colour‐emotion association and colour preference for elder care robot appearance

Colour‐emotion association and colour preference for elder care robot appearance

A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

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