Evidence map›Paper›PMID 41062660›Full record

ArticleScientific reports2025

Error-related potentials in EEG signals: feature-based detection for human-robot interaction.

Alessandra Fava, Valeria Villani, Lorenzo Sabattini

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alessandra FavaDepartment of Sciences and Methods of Engineering, University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy. alessandra.fava@unimore.it.
Valeria VillaniDepartment of Sciences and Methods of Engineering, University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy.
Lorenzo SabattiniDepartment of Sciences and Methods of Engineering, University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores how to improve the detection of Error-Related Potentials (ErrPs), namely brain signals generated when a person perceives an unexpected action performed by an interacting agent. ErrPs are promising for improving interactions between humans and robots because they offer a way for robots to understand the user's needs and expectations without explicit input. The proposed method aims at characterizing ErrP signals using a wide set of features extracted from electroencephalography (EEG) data, collected from subjects performing different tasks. This feature-based method results more accurate and efficient than traditional approaches, especially when applied to multiple users, or across different experimental setups. This work paves the way to feature-based ErrP detection to enhance human-robot interaction in dynamic environments.

Indexed as

BrainElectroencephalographyRoboticsAdultAlgorithmsFemaleHumansMaleSignal Processing, Computer-AssistedCross-subjects classificationEEGErrPMachine learningSelection of features

Identifiers

PMID41062660
PMCPMC12508437

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.