Predicting Dyslexia and ADHD using fMRI data

Classification methods, usually based on a definition of distance between two points. When representing the information we want to classify as covariance matrices, using the definition of the Riemannian distance instead of the Euclidean, produce higher success rates.

When classifying these EEG signals from the brain, even when using Riemann geometry, it is still difficult to classify new information that comes from someone we do not have prior information about him.

Using calibration methods, as well as innovative lowering dimensions methods, the results are significantly improve, and success rates are quite high.

Predicting Dyslexia and ADHD using fMRI data

 

 

Predicting Dyslexia and ADHD using fMRI data
Predicting Dyslexia and ADHD using fMRI data
Collaboration:

In Collaboration with Prof. Tzipi Horowitz-Kraus,

Dept. of Education in Science and Technology, Technion