Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit

Adding a personalized multi-modal access protocol to customize a BCI-FIT access method configuration for each individual end user, based on a combination of user characteristics, clinical expertise, user feedback, and system performance data in the software.

Adding a BCI-FIT adaptive signal modeling that employs transfer learning and on-line model adaptation techniques with noisy labels in the software of this brain-computer interface to eliminate the need for data collection exclusively for model calibration, as well as to address model drift issues associated with drowsiness, fatigue, and other human and environmental factors.

Adding BCI-FIT active querying techniques which are software-based optimal action control policies in the brain-computer interface developed with active and reinforcement learning techniques in order to perform efficient user intent inference to improve the entire speed-accuracy trade-off curve for alternative communication.

Adding vocabulary and location information (called partner and environmental input) to the language models in the brain-computer interface from a user's communication partner.