Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.

We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data.

Predictive Feedback, Early Sensory Representations, and Fast Responses to Predicted Stimuli Depend on NMDA Receptors.

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way).

High Spatial Resolution Imaging Mass Spectrometry Reveals Chemical Heterogeneity Across Bacterial Microcolonies.

Microbes interact with the world around them at the chemical level. However, directly examining the chemical exchange between microbes and microbes and their environment, at ecological scales, i.e.

Evoked Alpha Power is Reduced in Disconnected Consciousness During Sleep and Anesthesia.

Sleep and anesthesia entail alterations in conscious experience. Conscious experience may be absent (unconsciousness) or take the form of dreaming, a state in which sensory stimuli are not incorporated into conscious experience (disconnected consciousness). Recent work has identified features of cortical activity that distinguish conscious from unconscious states; however, less is known about how cortical activity differs between disconnected states and normal wakefulness.