We develop a method that is based on processing gathered Event Related Potentials (ERP) signals and the use of machine learning technique for multivariate analysis (i.e. classification) that we apply in order to analyze the differences between Dyslexic and Skilled readers.No human intervention is needed in the analysis process. This is the state of the art results for automatic identification of Dyslexic readers using a Lexical Decision Task. We use mathematical and machine learning based techniques to automatically discover novel complex features that (i) allow for reliable distinction between Dyslexic and Normal Control Skilled readers and (ii) to validate the assumption that most of the differences between Dyslexic and Skilled readers are located in the left hemisphere.Interestingly, these tools also pointed to the fact that High Pass signals (typically considered as "noise" during ERP/EEG analyses) in fact contain significant relevant information.Finally, the proposed scheme can be used for analysis of any ERP based studies.
|Title of host publication||2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Jul 2020|
|Event||2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Conference||2020 International Joint Conference on Neural Networks, IJCNN 2020|
|Period||19/07/20 → 24/07/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS This work was partially supported by a grant for computational equipment by the Caesarea Rothschild Institute and by a Hardware Grant by NVIDIA Corporation to the Neurocomputation Laboratory.
© 2020 IEEE.
- Classification of Event Related Potentials (ERP)
- Dyslexia classification
- Feature Extraction
- Feature Selection
- Machine Learning
ASJC Scopus subject areas
- Artificial Intelligence