77 research outputs found
The effect of enteral and perenteral nutrition on blood and respiratory tract immunoglobulins in intensive care unit patients
The Combined Use of Imaging Approaches to Assess Drug Release from Multicomponent Solid Dispersions
Design and optimization of self-microemulsifying drug delivery system (SMEDDS) of felodipine for chronotherapeutic application
Synthesis of folate- pegylated polyester nanoparticles encapsulating ixabepilone for targeting folate receptor overexpressing breast cancer cells
Rapid Assessment of Homogeneity and Stability of Amorphous Solid Dispersions by Atomic Force Microscopy—From Bench to Batch
Solid dispersions with hydrogenated castor oil increase solubility, dissolution rate and intestinal absorption of praziquantel
Electrospinning of Gelatin/Poly (Vinyl Pyrrolidone) Blends from Water/Acetic Acid Solutions
The Teaching of Foreign Languages in European Primary Schools: Evaluating Innovation and Establishing Research Priorities
Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.
This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications
- …