  Generating continuous electroencephalography (EEG) signals through advanced
artificial neural networks presents a novel opportunity to enhance
brain-computer interface (BCI) technology. This capability has the potential to
significantly enhance applications ranging from simulating dynamic brain
activity and data augmentation to improving real-time epilepsy detection and
BCI inference. By harnessing generative transformer neural networks,
specifically designed for EEG signal generation, we can revolutionize the
interpretation and interaction with neural data. Generative AI has demonstrated
significant success across various domains, from natural language processing
(NLP) and computer vision to content creation in visual arts and music. It
distinguishes itself by using large-scale datasets to construct context windows
during pre-training, a technique that has proven particularly effective in NLP,
where models are fine-tuned for specific downstream tasks after extensive
foundational training. However, the application of generative AI in the field
of BCIs, particularly through the development of continuous, context-rich
neural signal generators, has been limited. To address this, we introduce the
Generative EEG Transformer (GET), a model leveraging transformer architecture
tailored for EEG data. The GET model is pre-trained on diverse EEG datasets,
including motor imagery and alpha wave datasets, enabling it to produce
high-fidelity neural signals that maintain contextual integrity. Our empirical
findings indicate that GET not only faithfully reproduces the frequency
spectrum of the training data and input prompts but also robustly generates
continuous neural signals. By adopting the successful training strategies of
the NLP domain for BCIs, the GET sets a new standard for the development and
application of neural signal generation technologies.
