Towards semantic brain-computer interfacing: The graded inventory of semantic triggers (GIST) paradigm.

Semantic Brain-Computer Interfaces (sBCIs) aim to decode conceptual information directly from neural activity, enabling more efficient and natural communication for individuals with severe motor impairments. However, existing approaches are often confounded by stimulus-driven perceptual processes and lack a reliable means of tagging the moment of concept formation. We introduce the Graded Inventory of Semantic Triggers (GIST), a novel electroencephalography (EEG) paradigm designed to elicit semantic retrieval from graded verbal clues with precise temporal alignment.

Approach: EEG data were recorded from 14 participants performing the GIST task across three semantic categories (food, musical instruments, and body parts). Target neural activity was aligned to each participant's self-reported moment of semantic retrieval (spacebar press), enabling response-locked analysis. Here, we propose a staged framework where the first comprises a Conformer-based encoder trained on pooled data using a hybrid contrastive objective that combines instance-level consistency and cross-participant class alignment with domain-adversarial learning to reduce participant-specific information. In the second stage, the frozen encoder is transferred to a held-out participant whose data played no part in Stage 1, and a support vector machine is calibrated on that participant's own labelled items under a nested leave-one-item-out protocol.


Results

Above-chance semantic decoding was achieved in all three pairwise tasks. Mean accuracies were 73.4% (Body vs. Food), 70.7% (Body vs. Music), and 68.2% (Food vs. Music). Participant-level permutation tests were significant in 9/11 participants for Body vs. Food and 8/11 for each of the other tasks (p < 0.05). The pre-response interval at [-1.5s, -0.5s] was the most discriminative.&#xD;&#xD;Significance: The GIST paradigm enables controlled elicitation and temporal alignment of semantic processing while reducing perceptual confounds. Combined with cross-participant representation pretraining and participant-specific calibration, the results demonstrate above-chance decoding of semantic categories from EEG in individuals excluded from representation learning, supporting the feasibility of sBCIs.