Clinical electroencephalogram (EEG) reports encode domain-specific linguistic conventions that general-purpose language models (LMs) fail to capture. We introduce NeuroLex, a lightweight domain-adaptive language model trained purely on EEG report text from the Harvard Electroencephalography Database. Unlike existing biomedical LMs, NeuroLex is tailored to the linguistic and diagnostic characteristics of EEG reporting, enabling it to serve as both an independent textual model and a decoder backbone for multimodal EEG-language systems. Using span-corruption pretraining and instruction-style fine-tuning on report polishing, paragraph summarization, and terminology question answering, NeuroLex learns the syntax and reasoning patterns characteristic of EEG interpretation. Comprehensive evaluations show that it achieves lower perplexity, higher extraction and summarization accuracy, better label efficiency, and improved robustness to negation and factual hallucination compared with general models of the same scale. With an EEG-aware linguistic backbone, NeuroLex bridges biomedical text modeling and brain-computer interface applications, offering a foundation for interpretable and language-driven neural decoding.

A supercomputer whirs away in London, crunching complex drug chemistries into deep learning algorithms to discover new medications. A few miles away, a DeepMind neural network scans millions of images from Moorfields Eye Hospital, searching for signs of eye disease. The application casually asks if you still have that headache from yesterday and if you'd like to book a doctor's appointment for tomorrow. Of all the fields that artificial intelligence will disrupt in the coming years, healthcare may see the greatest paradigm shift. AI's influence in the industry will be deep and broad.

A supercomputer whirs away in London, crunching complex drug chemistries into deep learning algorithms to discover new medications. A few miles away, a DeepMind neural network scans millions of images from Moorfields Eye Hospital, searching for signs of eye disease. The application casually asks if you still have that headache from yesterday and if you'd like to book a doctor's appointment for tomorrow. Of all the fields that artificial intelligence will disrupt in the coming years, healthcare may see the greatest paradigm shift. AI's influence in the industry will be deep and broad.

People convey meaning by what they say as well as how they say it: Tone, word choice and the length of a phrase are all crucial cues to understanding what's going on in someone's mind. When a psychiatrist or psychologist examines a person, they listen for these signals to get a sense of their wellbeing, drawing on past experience to guide their judgment. Researchers are now applying that same approach, with the help of machine learning, to diagnose people with mental disorders. In 2015, a team of researchers developed an AI model that correctly predicted which members of a group of young people would develop psychosis--a major feature of schizophrenia--by analyzing transcripts of their speech. This model focused on tell-tale verbal tics of psychosis: short sentences, confusing, frequent use of words like "this," "that," and "a," as well as a muddled sense of meaning from one sentence to the next.