Scholarlink

<p>This study introduces a quantum-inspired and self-organized model for EEG-based language detection. The Raussendorf Lattice Pattern (RLP) is proposed as a graph-based feature extractor inspired by quantum topology. It defines 15 energy-based patterns that adapt to signal dynamics and generate explainable texture features. A five-level Twin Wavelet Transform produces 18 wavelet bands for multilevel frequency-domain features. Statistical and RLP features are fused into a single vector. Iterative NCA selects the most discriminative features, while kNN and SVM perform channel-wise classification. Iterative Majority Voting fuses outputs for optimal accuracy. The model achieves 99.54% (tenfold CV) and 92.84% (LOSO) accuracies. Semantic cortical maps show dominant frontal activation near Broca’s area. The results confirm that quantum-inspired self-organized feature extraction offers efficient and explainable solutions for EEG-based inner-speech and language detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated language detection system using Raussendorf lattice pattern features with EEG signals

  • 期刊论文

  • 发表日期：

    2025年11月29日

查看全文
  • Sengul Dogan,
  • Turker Tuncer,
  • Prabal Datta Barua,
  • Ru-San Tan,
  • U. R. Acharya

摘要

This study introduces a quantum-inspired and self-organized model for EEG-based language detection. The Raussendorf Lattice Pattern (RLP) is proposed as a graph-based feature extractor inspired by quantum topology. It defines 15 energy-based patterns that adapt to signal dynamics and generate explainable texture features. A five-level Twin Wavelet Transform produces 18 wavelet bands for multilevel frequency-domain features. Statistical and RLP features are fused into a single vector. Iterative NCA selects the most discriminative features, while kNN and SVM perform channel-wise classification. Iterative Majority Voting fuses outputs for optimal accuracy. The model achieves 99.54% (tenfold CV) and 92.84% (LOSO) accuracies. Semantic cortical maps show dominant frontal activation near Broca’s area. The results confirm that quantum-inspired self-organized feature extraction offers efficient and explainable solutions for EEG-based inner-speech and language detection.