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August 18, 2025Journal of Neural Engineering

Towards stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients

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Authors

AVAlexey VoskoboinikovMAMagomed AliverdievJNJulia Nekrasova

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Overview

Machine learning advances improve speech mapping accuracy in epilepsy and brain tumor patients, indicating safer procedures.

Key Points

  • Machine learning approaches achieved high accuracy for speech-related mapping in patients, avoiding traditional stimulation risks.
  • Using Linear Support Vector Classification, ROC-AUC and PR-AUC scores reached 0.91 and 0.88, enabling effective channel distinction.
  • The analysis utilized a comprehensive dataset with temporal features from 14 patients with stereo-EEG electrodes.
  • Findings highlight the potential for safer, reliable mapping procedures in neurosurgery, supporting patient safety and outcomes.

Cite This Study

Voskoboinikov et al. (2025) studied this question.

synapsesocial.com/papers/68af454cad7bf08b1ead33ebhttps://doi.org/10.1088/1741-2552/adfc9c
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1CortiQ-based Real-Time Functional Mapping for Epilepsy Surgery2015 · 31 citations
  2. 2Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery2020 · 42 citations
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  4. 4Spatial-temporal functional mapping of language at the bedside with electrocorticography2016 · 54 citations
  5. 5Electrical Stimulation Mapping of the Brain: Basic Principles and Emerging Alternatives2018 · 118 citations