Eeg GPT

EEG-based research is increasingly leveraging advanced machine learning techniques, particularly deep learning architectures, to improve the accuracy and interpretability of brain signal analysis. Current efforts focus on developing novel neural network models, including graph convolutional networks (GCNs), temporal convolutional networks (TCNs), and the integration of large language models (LLMs) like those used in GPT, to enhance EEG classification for various applications such as brain-computer interfaces (BCIs) and neurological disease diagnosis. These advancements aim to overcome limitations of traditional methods by improving classification accuracy, reducing noise, and providing more transparent and reliable results, ultimately leading to more effective and trustworthy brain-computer interfaces and clinical tools.