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3 results on '"error potential (ErrP)"'

1. Estudio de la variabilidad entre repeticiones del potencial de error respecto a P300 y N500

Author

Soriano-Segura, Paula, Gracia, Desirée Irene, Ferrero, Laura, Iáñez, Eduardo, Ortiz, Mario, Azorín, José M., Soriano-Segura, Paula, Gracia, Desirée Irene, Ferrero, Laura, Iáñez, Eduardo, Ortiz, Mario, and Azorín, José M.

Abstract

[Resumen] La utilización del potencial de error (ErrP) para autocorregir los comandos erróneos de las Interfaces Cerebro-Máquina (BMI) ha despertado gran interés, dado que ayuda a incrementar la precisión del sistema. Por este motivo, lograr una clasificación precisa en cada repetición es crucial para una implementación efectiva de BCI. Este estudio se centra en el análisis de la variabilidad del ErrP entre repeticiones de una tarea, generado por un estímulo visuotáctil cuando un exoesqueleto de miembro inferior se activa para iniciar la marcha en contra de su voluntad. Concretamente, se examinan los valores en tiempo y amplitud de los picos P300 y N500 del ErrP, así como la diferencia entre ellos para cada repetición. Posteriormente, el Error Cuadrático Medio (RMSE) cuantifica las diferencias entre las características, permitiendo conocer la dispersión que existe entre repeticiones., [Abstract] The use of the error potential for self-correction in brain-computer interfaces (BCIs) has gained significant attention, as it contributes to increasing the system’s accuracy. Therefore, achieving an accurate classification on a single-trial basis is crucial for an effective BCI implementation. This study focuses on analyzing the variability in single trial of the ErrP, elicited by visuotactile feedback when a lower limb exoskeleton activates to start the gait against subject’s will. Specifically, the temporal and amplitude values of the P300 and N500 peaks within the error potential, as well as the difference between them, are examined. Subsequently, the root mean square error (RMSE) is calculated to quantify the discrepancies between trials features, providing insights into the dispersion among the trials.

Published
2023

2. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

Author

Kreilinger, Alex, Hiebel, Hannah, and Muller-Putz, Gernot R.

Subjects
*BRAIN-computer interfaces, *ERROR detection (Information theory), *ELECTROENCEPHALOGRAPHY, *FEEDBACK control systems, *MOTOR cortex
Abstract

Objective: This work aimed to find and evaluate a new method for detecting errors in continuous brain–computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. Methods: In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. Results: We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Conclusion: Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. Significance: This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates. [ABSTRACT FROM PUBLISHER]

Published
2016
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3. Investigation of a hybrid brain-computer interface (BCI) for optimal design as a human-machine interface

Author

Mindermann, Björn, Gräser, Axel, and Basar-Eroglu, Canan

Subjects
minimum-energy-combination (MEC), 7 degrees of freedom (DOF), eye artifacts, brain-computer interface (BCI), event-related potential (ERP), 620 Engineering, human-machine interface (HMI), minimum distance to riemannian mean (MDRM), robot arm, error potential (ErrP), steady-state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS), alpha waves, electroencephalography (EEG), ddc:620, P300, maximum-contrast-combination (MCC)
Abstract

People with partial or complete paralysis are usually dependent on nursing staff to cope with their daily activities. Alternatively, an assistance robotics system serves as a substitute for the lost motor skills and enables an improvement in the quality of life. With the help of a human-machine interface, the user can operate the system and initiate autonomous processes. If the system consists of a robot arm, the disabled person is able to grab and move objects. In the case of an error caused by the system, the user should be able to control the robot arm directly. With the interface developed in this work, a human being can open or close as well as move the robot gripper into any position and orientation. Since the target group of the system is people with very limited body movements, a Brain-Computer Interface (BCI) is used for communication. It establishes a communication channel between the human brain and the controlled robot arm. A special helmet is used for recording the brain signals, which, in addition to the measuring electrodes, has a stimulator in the peripheral field of vision of the user. This stimulator has four stimuli by which an evocation of event-related and visual potentials are provided. By detecting Steady State Visual Evoked Potentials (SSVEPs) and P300 potentials, the system can determine which stimulus the user has focused on. With this, four discrete commands are provided for the user to control the robot. For switching the stimulator on and off motor imaginations are used. A state machine with a control group for each translational and rotational movement is used to control the robot. A stepwise control, in which the gripper performs discrete steps, and a speed-based control, in which the movement of the gripper is started and stopped has been implemented. Switching between groups is performed by the sequential sending of two commands. In each group, the user can perform a movement in the positive and negative direction of the selected axis, change the step width or speed, and leave the group. Stopping at the target position and in dangerous situations occurs by closing the eyes and the thereby occurring artifacts and alpha waves in the signals. Both control concepts were tested in a study and evaluated with objective and subjective criteria. The experiments have shown that BCI-based robot arm control is feasible and useful.

Published
2018

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