Standardizing Clinical EEG for Language Modeling

The EEG-to-Report framework addresses the fundamental challenge of training language models on clinical electroencephalogram (EEG) data: the lack of a structured, machine-readable bridge between raw neural waveforms and human-written clinical diagnostic reports. By developing a specialized annotation and feature-text mapping system, the authors create a pipeline that allows LLMs to interpret complex EEG signals as linguistic or structured data, facilitating automated report generation and clinical decision support.

The Annotation and Feature-Text Framework

The core innovation lies in the transformation of raw, high-dimensional EEG time-series data into a format compatible with transformer-based architectures. The framework utilizes:

• Feature Extraction: A systematic approach to isolating clinically relevant biomarkers from raw EEG signals, reducing noise while preserving diagnostic information.
• Annotation Mapping: A structured schema that aligns specific neural patterns with standardized clinical terminology found in professional EEG reports.
• Textual Alignment: By converting these annotated features into a text-based representation, the framework enables the use of standard LLM training objectives, allowing models to learn the causal and correlative relationships between brain activity and clinical findings.

This approach overcomes the limitations of traditional black-box neural network models by providing an interpretable, feature-rich input layer that aligns with the way clinicians document patient conditions.