Neural Decoding

Neural decoding aims to translate brain activity patterns, measured using techniques like fMRI, EEG, and ECoG, into meaningful information about cognitive states, sensory experiences, or motor intentions. Current research emphasizes improving decoding accuracy and robustness using advanced machine learning models, including deep learning architectures like convolutional and recurrent neural networks, and large language models (LLMs) prompted by brain activity representations. These advancements hold significant promise for improving brain-computer interfaces, furthering our understanding of brain function, and enabling novel applications in neuroprosthetics and neurorehabilitation.