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Related Experiment Video

Updated: May 9, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
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Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.

Alex Brandmeyer1, Jason D R Farquhar, James M McQueen

  • 1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. a.brandmeyer@donders.ru.nl

Plos One
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

This study shows that brain-computer interfaces (BCIs) can decode speech perception from EEG data. BCIs successfully identified phoneme categories and even a user's native language, paving the way for new learning tools.

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Related Experiment Videos

Last Updated: May 9, 2026

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Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Published on: October 22, 2015

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Machine Learning

Background:

  • Brain-computer interfaces (BCIs) analyze neuroimaging data for real-time user mental state assessment, often for neurofeedback.
  • Investigating BCIs for speech perception is crucial for understanding auditory processing and developing assistive technologies.

Purpose of the Study:

  • To assess the feasibility of a BCI system for decoding speech perception from single-trial EEG data.
  • To determine if phoneme category differences can be decoded at the single-trial level.
  • To investigate cross-participant decoding of phoneme categories within and between native-language groups.

Main Methods:

  • Applied multivariate pattern classification to single-trial electroencephalography (EEG) data during speech perception tasks.
  • Collected data from both native and non-native speakers of a language.
  • Analyzed classification performance based on stimulus contrast, participant's native language, and cross-participant training.

Main Results:

  • Classification performance increased with the categorical status of the stimulus contrast (within, boundary, across).
  • Native language was decoded from EEG data with over 80% accuracy.
  • Cross-participant analysis showed improved performance when trained on data from all participants, but native-language-specific training benefited native speakers more.

Conclusions:

  • Electrophysiological responses during speech perception are decodable at the single-trial level using BCIs.
  • Decoding performance reflects graded changes related to phonological stimulus properties.
  • This BCI approach holds potential for applications like perceptual learning in second language acquisition.