Wave2Word: A Multimodal Transformer Framework for Joint EEG-Text Alignment and Multi-Task Representation Learning in Neurocritical Care

Continuous electroencephalography (EEG) is routinely used in neurocritical care to monitor seizures and other harmful brain activity, including rhythmic and periodic patterns that are clinically significant. Although deep learning methods have achieved high accuracy in seizure detection, most existing approaches remain seizure-centric, rely on discrete-label supervision, and are primarily evaluated using accuracy-based metrics. A central limitation of current EEG modeling practice is the weak correspondence between learned representations and how EEG findings are interpreted and summarized in clinical workflows. Harmful EEG activity exhibits overlapping patterns, graded expert agreement, and temporal persistence, which are not well captured by classification objectives alone. This work proposes a multimodal EEG representation learning framework that integrates signal-domain modeling with structured clinical language supervision. First, raw EEG is transformed into a longitudinal bipolar montage and time-frequency representations. Second, dual transformer-based encoders model complementary temporal and frequency-centric dependencies and are fused using an adaptive gating mechanism. Third, EEG embeddings are aligned with structured expert consensus descriptions through a contrastive objective. Finally, an EEG-conditioned text reconstruction loss is introduced as a representation-level constraint alongside standard classification loss. Experimental evaluation using a controlled train-validation-test split achieves a six-class test accuracy of 0.9797. Ablation analyses show that removing contrastive alignment reduces cross-modal retrieval performance from Recall@10 of 0.3390 to 0.0045, despite minimal change in classification accuracy. These findings demonstrate that discriminative accuracy does not reliably reflect representation quality for clinically meaningful EEG modeling.