Binary EEG Control for Two-Dimensional Cursor Movement: An Online Approach

Kayagil, Turan A; Bai, Ou; Lin, Peter; Furlani, S.; Vorbach, Sherry; Hallett, Mark

doi:10.1109/iccme.2007.4382005

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…[ 10 ]. In 2007, Kayagil et al explored a binary approach using the binary cursor control paradigm to achieve more complex controls by simply answering yes or no [ 11 ]. Wu and others designed an Ethernet robot to implement actions for disabled patients [ 12 ].…”

Section: A Survey On Robotic Control Applications Using Noninvasive M...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: A Survey On Robotic Control Applications Using Noninvasive M...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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“…Most of the BCI-based games employ electroencephalography (EEG) for online detection of player's motive, self-assessment about his performance [5], learning skill to improve performance [6], automatic training of external manipulators/robots by BCI-based learning strategies [7,8], and the like. Existing BCI-gaming applications employ different EEG signals, such as Event Related De-synchronization/Event Related Synchronization (ERD/ERS) [9,10], P300 [11,12], Steady-State Visual Evoked Potential (SSVEP) [13,14], neuro/biofeedback [1] and hybrid paradigms [15] to address different problems in computer games. In recent times, the authors used left/right motor imagery to control cursors [16], paddles in pinball games [17] and robotic manipulators [8], to improve success-rate in game outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Early BCI was restricted to multi-trial analysis. However, for real-time gaming applications, single-trial EEG BCI is emphasized [9][10][11][12][13][14][15] over its multi-trial counterparts.…”

Section: Introductionmentioning

confidence: 99%

EEG-Induced Autonomous Game-Teaching to a Robot Arm by Human Trainers Using Reinforcement Learning

et al. 2022

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This paper deals with a simple indoor game, where the player has to pass a ball through a ring fixed on a variable pan-tilt platform. The motivation of the research is to learn the gaming actions of an experienced player by a robot arm for subsequent training to younger children (trainee) by the robot. The robot learns the gaming actions of the player at different game states, determined by pan-tilt orientations of the ring and its radial distance with respect to the player. The actions of the experienced player/expert are defined by six parameters: three junction-coordinates in the right arm of the player and the 3dimensional speed of the ball in a given throw. Reinforcement learning is employed here to adapt a state-action probability matrix of a probabilistic learning automation based on the reward (or penalty) scores of the player due to the success (or failure) in passing the ball through a given ring. A hybrid braincomputer interface (BCI) is used to detect the failures in the gaming action of the player by natural arousal of Error-related Potential (ErrP) signal following motor execution, indicated by motor imageries. In absence (presence) of ErrP after a motor imagination, the system considers a success (failure) in the player's trials, and thus adapts the probabilities in the learning automata according to success/failure of individual game instances. After the convergence of the state-action probability matrix, the same is used for planning, where the action corresponding to the highest probability at a given state in the automaton is selected for execution. The robot can autonomously train the game to the children using the learning automaton with converged probability scores. Experiments undertaken confirm that the success rate of the robot arm in the motor execution phase is very high (above 90%) when the ring is placed at a moderate distance of 4 feet from the robot.

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“…EEG is cheaper and non-invasive (Kayagil et al, 2007 ; Zanzotto and Croce, 2010 ; Illman et al, 2020 ; Tait et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

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Binary EEG Control for Two-Dimensional Cursor Movement: An Online Approach

Cited by 11 publications

References 6 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

EEG-Induced Autonomous Game-Teaching to a Robot Arm by Human Trainers Using Reinforcement Learning

Decoding Covert Speech From EEG-A Comprehensive Review

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