Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration
This work addresses the problem of user variability in brain-computer interfaces for scalable systems, offering an incremental improvement with practical calibration strategies.
The study tackled the challenge of cross-subject generalization in EEG-based imagined speech decoding by proposing a cyclic inter-subject training approach with lightweight calibration, achieving an accuracy of 0.781 and AUC of 0.801 using only 10% of target subject data.
Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activity patterns. This study examined how training dynamics and lightweight subject specific adaptation influence cross subject performance in a neural decoding framework. A cyclic inter subject training approach, involving shorter per subject training segments and frequent alternation among subjects, led to modest yet consistent improvements in decoding performance across unseen target data. Furthermore, under the subject calibrated leave one subject out scheme, incorporating only 10 % of the target subjects data for calibration achieved an accuracy of 0.781 and an AUC of 0.801, demonstrating the effectiveness of few shot adaptation. These findings suggest that integrating cyclic training with minimal calibration provides a simple and effective strategy for developing scalable, user adaptive brain computer interface systems that balance generalization and personalization.