Cueless EEG imagined speech for subject identification: dataset and benchmarks

2025-01-16

AI Summary

This study addresses the challenge of EEG-based biometric identification in a cue-free paradigm. Unlike conventional approaches relying on auditory or visual stimuli, we propose a novel identity recognition framework grounded in self-initiated imagination of semantically rich vocabulary. We introduce the first cue-free EEG imagined speech dataset—comprising 11 subjects and over 4,350 trials—in which participants mentally rehearse semantically explicit words without external prompting. Methodologically, we integrate multiple classifiers—including SVM, XGBoost, EEG-Conformer, Shallow ConvNet, and foundational time-series models—and employ session-level hold-out validation. Our approach achieves a cross-session identity classification accuracy of 97.93%, substantially enhancing the ecological validity and practical deployability of EEG biometrics. This work establishes a new pathway for secure, non-invasive authentication in brain–computer interface applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Bio-inspired Learning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification. While previous studies have explored the use of imagined speech with semantically meaningful words for subject identification, most have relied on additional visual or auditory cues. In this study, we introduce a cueless EEG-based imagined speech paradigm, where subjects imagine the pronunciation of semantically meaningful words without any external cues. This innovative approach addresses the limitations of prior methods by requiring subjects to select and imagine words from a predefined list naturally. The dataset comprises over 4,350 trials from 11 subjects across five sessions. We assess a variety of classification methods, including traditional machine learning techniques such as Support Vector Machines (SVM) and XGBoost, as well as time-series foundation models and deep learning architectures specifically designed for EEG classification, such as EEG Conformer and Shallow ConvNet. A session-based hold-out validation strategy was employed to ensure reliable evaluation and prevent data leakage. Our results demonstrate outstanding classification accuracy, reaching 97.93%. These findings highlight the potential of cueless EEG paradigms for secure and reliable subject identification in real-world applications, such as brain-computer interfaces (BCIs).

Problem

• EEG
• Biometrics
• Mental Imagery Recognition

Innovation

• EEG Biometrics
• Imagined Word Recognition
• Advanced Classification Algorithms