Neural Memory Decoding with EEG Data and Representation Learning
This addresses the problem of brain-computer interfaces for memory decoding and information retrieval, but it is incremental as it builds on existing representation learning methods.
The paper tackles the problem of decoding memory from EEG data, achieving an average top-1 accuracy of 78.4% (chance 4%) in identifying recalled concepts, and applies this to neural information retrieval for document retrieval.
We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space. Because representation learning is used, concepts can be identified even if they do not appear in the training data set. However, reference EEG data must exist for each such concept. We also show an application of the method to the problem of information retrieval. In neural information retrieval, EEG data is captured while a user recalls the contents of a document, and a list of links to predicted documents is produced.