BMI-based framework for teaching and evaluating robot skills

Peñaloza, Christian; Mae, Yasushi; Kojima, Masaru; Arai, Tatsuo

doi:10.1109/icra.2014.6907749

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…Recently, three new types of errors: target, outcome, and execution ErrPs have been reported [44], [45]. The vast majority of studies, with a focus on ErrP-BCI for rehabilitation and assistance to motor-impaired people in ADL, have utilized interaction and observation ErrP in their BCIs [6], [27], [30], [31], [46]- [49].…”

Section: Error-related Potentialsmentioning

confidence: 99%

“…Penaloza et al [104] used a similar strategy in a navigation task and utilized the ERN signal as an emergency signal to stop the robot's navigation due to any perceived threat. In another study, Penaloza et al [46] used human error perception to evaluate a robot's learning performance in an object manipulation task. In their framework, the authors teleoperated the robot and utilized a probability-based object selection task which selects the second-best action if the first action causes an ERN signal to be elicited.…”

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

“…In their framework, the authors teleoperated the robot and utilized a probability-based object selection task which selects the second-best action if the first action causes an ERN signal to be elicited. Notably, Penaloza et al [104] used a low-cost commercially available EEG headset, Emotiv EPOC in their study [46], [104] and reported an ERN classification rate as high as 82%. A step further, Salazar-Gomez et al [43] reported how a secondary ErrP signal can be elicited when the system wrongly classifies the ERN signal as the CRN and vice versa and can improve the performance of a robot performing a binary choice task.…”

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

“…For instance, Ehrlich and Cheng [24] employed an ErrP signal for the co-adaptation of a robot's gaze behavior pattern in the object selection task, in which a user tried to estimate the object that the robot would select from its gazing patterns. Unlike previous studies [43], [46], [50], [52], [104], in which the participant is the master and the robot must adapt according to the user perspectives, in this study, the optimal behavior of the robot was negotiated between the user and the robot in a co-adaptive fashion. With this approach, the efficacy of correct estimation of the robot's behavior increased from the initial chance-level (∼33%) to 70-90% within 10-40 trials.…”

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

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A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

et al. 2019

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Regaining the lost functionality of limbs is the top priority for people with motor skills impairment as it directly affects their ability to execute activities of daily living and hence, worsens their quality of life. In the last two decades, a great deal of research has focused on error-related potential (ErrP) based brain-computer interfaces (BCIs). Many applications have been developed to assist motor-impaired people in their rehabilitation and among these are robots, spellers, gesture recognition systems, and braincontrolled wheelchairs. In this paper, we present a review of various ErrP based BCI that can potentially aid motor-disabled people in their rehabilitation and execution of their daily activities. First, we describe the ErrP phenomenon and its characteristics followed by a comprehensive application-driven discussion on ErrP based rehabilitation and assistive strategies for motor-impaired people, including studies conducted since the inception of ErrP to the current state-of-the-art applications. Lastly, we discuss the potential issues and challenges being faced by current state-of-the-art applications as well as important future pathways and research directions that might be adopted for advanced ErrP-BCIs used in clinical settings.

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Section: Error-related Potentialsmentioning

confidence: 99%

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

Section: Error-related Potentials Based Bci For Motor Impaired Populationmentioning

confidence: 99%

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A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

et al. 2019

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“…Amidst the applications found in the literature review, some novelties as the one presented in [4], where a BCI aimed at teaching tasks to a humanoid robot regarding object manipulation (e.g. according to visual characteristics as color) are put forward.…”

Section: Introductionmentioning

confidence: 99%

Control of a Mobile Robot Through Brain Computer Interface

Moreno¹,

Alemán

2015

Inge cuc

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Abstract-This paper poses a control interface to command the movement of a mobile robot according to signals captured from the user's brain. These signals are acquired and interpreted by Emotiv EPOC device, a 14-electrode type sensor which captures electroencephalographic (EEG) signals with high resolution, which, in turn, are sent to a computer for processing. One brain-computer interface (BCI) was developed based on the Emotiv software and SDK in order to command the mobile robot from a distance. Functionality tests are performed with the sensor to discriminate shift intentions of a user group, as well as with a fuzzy controller to hold the direction in case of concentration loss. As conclusion, it was possible to obtain an efficient system for robot movements. Keywords-Brain Computer Interface -BCI, EmotivEpoc, Mobile Robot, Arduino, EEG.Resumen-En este artículo se presenta una interfaz de control que permite comandar el movimiento de un robot móvil en función de la captura de señales provenientes del cerebro del usuario. Dichas señales son adquiridas e interpretadas por medio del dispositivo Emotiv Epoc, el cual cuenta con 14 sensores tipo electrodo que captan señales electroencefalográficas (EEG) de alta resolución, que después son enviadas a un equipo de cómputo para ser procesadas. Se desarrolla una interfaz cerebro-computador (BCI) basada en el software y SDK del desarrollador del Emotiv mediante la cual se comanda de forma remota el robot móvil. Se realizan pruebas de funcionalidad con el sensor para discriminar una intención de desplazamiento por parte de un grupo de usuarios y un controlador difuso para sostener la dirección en casos de perdida de la concentración. Como conclusión, se logra obtener un sistema eficiente para la manipulación del robot.

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10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

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BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

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BMI-based framework for teaching and evaluating robot skills

Cited by 5 publications

References 16 publications

A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

Control of a Mobile Robot Through Brain Computer Interface

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

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