Decoding inner speech with functional connectivity

Abstract Background: Inner-Speech (IS) based Brain-Computer Interfaces (BCIs) offer potential communication solutions for individuals with disabilities by decoding brain signals generated during speech imagination. While most IS-BCI systems rely on time-frequency EEG features, this study investigates functional connectivity—specifically, motif synchronization (MS)—to determine whether interactions between brain regions improve the discrimination of imagined words. Methods : We analyzed Electroencephalography (EEG) data from the ‘Thinking Out Loud’ dataset by Nieto et al 2022, involving 10 participants mentally simulating four Spanish words (‘Up,’ ‘Down,’ ‘Left,’ ‘Right’). Connectivity matrices were derived using the MS method, and node metrics (strength, PageRank) were calculated across 11 frequency bands. A two-stage classification framework was implemented: a support vector machine (SVM) ranked features by discriminatory power, followed by 5-fold cross-validation to identify optimal feature combinations. Results: The proposed approach achieved an average classification accuracy of 43.7%, comparable to previously reported results on the same dataset. Conclusions: Motif synchronization–based functional connectivity and graph metrics provide informative features for imagined speech BCIs by capturing cross-regional brain interactions. Further work is needed to improve robustness and generalization across sessions and subjects.