Event-related potential

Summary

An event-related potential (ERP) is the measured brain response that is the direct result of a specific sensory, cognitive, or motor event. More formally, it is any stereotyped electrophysiological response to a stimulus. The study of the brain in this way provides a noninvasive means of evaluating brain functioning. ERPs are measured by means of electroencephalography (EEG). The magnetoencephalography (MEG) equivalent of ERP is the ERF, or event-related field. Evoked potentials and induced potentials are subtypes of ERPs. History With the discovery of the electroencephalogram (EEG) in 1924, Hans Berger revealed that one could measure the electrical activity of the human brain by placing electrodes on the scalp and amplifying the signal. Changes in voltage can then be plotted over a period of time. He observed that the voltages could be influenced by external events that stimulated the senses. The EEG proved to be a useful source in recording brain activity over the ensui

Official source

https://en.wikipedia.org/wiki/Event-related_potential

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (79)

Related publications

Related people (11)

Related people

Related units (8)

Related units

Related concepts (20)

Related concepts

Related courses (6)

Related courses

Related lectures (4)

Related lectures

Error-Related EEG Potentials Generated during Simulated Brain-Computer Interaction

Pierre Ferrez

Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: "Are ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?" We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. But in order to exploit these ErrP we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively using a classifier built with data recorded up to three months earlier.

2008

Brain-computer interfaces (BCI), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Most of these studies show the presence of ErrP in typical choice reaction tasks where subjects respond to a stimulus and ErrP arise following errors due to the subject's incorrect motor action. However, in the context of a BCI, the central question is: "Are ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?" We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the interface and no longer errors of the subject himself. Four healthy volunteer subjects participated in a simple human-robot interaction experiment (i.e., bringing the robot to either the left or right side of a room), which seem to reveal a new kind of ErrP. These "interaction ErrP" exhibit a first sharp negative peak followed by a broader positive peak and a second negative peak (~270, 400 and 550 ms after the feedback, respectively). But in order to exploit these ErrP we need to detect it in each single trial using a short window following the feedback that shows the response of the classifier embedded in the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.7% and 80.2%, respectively. We also show that the integration of these ErrP in a BCI, where the subject's intent is not executed if an ErrP is detected, significantly improves the performance of the BCI.

The MIT Press2007

Towards everyday brain-computer interfacing through decoding visual cognition

Ruslan Aydarkhanov

Decoding visual cognition from non-invasive measurements of brain activity has shown valuable applications. Vision-based Brain-Computer Interfaces (BCI) systems extend from spellers to database search and spatial navigation. Despite the high performance of these systems, they are limited to the laboratory environment. Several attempts were made to improve the quality of life of patients with severe neurodegenerative disorders by helping them acquire more independence. Bringing vision-based BCI to everyday life would benefit a wide range of population from handicapped to healthy people. However, real-life applications impose additional challenges on obtaining neural signals with a good signal-to-noise ratio. The natural environments are rich, dynamic and ambiguous that challenges the visual perception and, therefore, the decoding of underlying neural correlates. The limited attentional resources require to explore and visually sample the environment by freely moving eyes and fixating on the relevant objects. Moreover, everyday activities are rarely isolated leading to a mixture and complex interactions between neural correlates in the measured signal. In this thesis, I explore various aspects of decoding brain activity by developing new protocols for realistic scenarios with challenging visual tasks. The tasks include free visual exploration, dynamic scenes, and multiple tasks, for example, recognition of a billboard category during simulated driving. The major efforts have been directed towards synchronous decoding of Event-Related Potentials (ERP) acquired by the means of electroencephalography (EEG) and Eye-tracking technology, which allowed the extraction of Eye-Fixation Related Potentials (EFRP). Additionally, I investigate the neural correlates of perceptual decision making which is tightly linked to visual recognition. The perceptual challenges can lead to higher temporal variability of obtained ERP which was also addressed from the decoding perspective. The obtained results, firstly, provide new insights on how to tackle the temporal variability of ERP. Secondly, they raise new questions on studying and decoding neural correlates of perceptual decision making. And thirdly, they show the feasibility of decoding visual recognition in a simulated realistic scenario of car driving. Although, as expected, I found a lower performance compared to classical ERP protocols, these findings hold promise and raise new questions to be investigated in order to improve the quality of decoding visual cognition for everyday application.

EPFL2020

Towards everyday brain-computer interfacing through decoding visual cognition

Ruslan Aydarkhanov

EPFL2020