Source Data Selection for Brain-Computer Interfaces based on Simple Features
This paper demonstrates that simple features available during the calibration of a brain-computer interface can be utilized for source data selection to improve the performance of the brain-computer interface for a new target user through transfer learning. To support this, a public motor imagery dataset is used for analysis, and a method called the Transfer Performance Predictor method is presented. The simple features are based on the covariance matrices of the data and the Riemannian distance between them. The Transfer Performance Predictor method outperforms other source data selection methods as it selects source data that gives a better transfer learning performance for the target users.
Code (0)
등록된 구현이 없습니다.
Tasks
Brain Computer InterfaceMotor ImageryTransfer LearningSimilar Papers 제목 키워드 기반
Information Theoretic Feature Transformation Learning for Brain Interfaces
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence…
Brain Computer InterfaceDimensionality ReductionEEGElectroencephalogram (EEG)+2Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button
A brain-computer interface (BCI) allows users to “communicate” with a computer without using their muscles. BCI based on sensori-motor rhythms use imaginary motor tasks, such as moving the right or left hand to send cont…
Brain Computer InterfaceGeneral ClassificationBayesian Networks for Brain-Computer Interfaces: A Survey
Brain-Computer Interface (BCI) is a rapidly developing technology that allows direct communications between the human brain and external devices, such as robotic arms and computers. Bayesian Networks is a powerful tool i…
Brain Computer InterfaceSurveyMethod for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
This paper provides a brief introduction of the mathematical theory behind the time series unfolding method. The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from…
Brain Computer InterfaceTime SeriesInformation-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces
Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using a sequence of sensory stimuli to elicit specific neural responses as control signals, while a user attends to relevant target stim…
Decision Making