K-sets of neural networks and its application on motor imagery classification

Author(s)

Denis Renato de Moraes Piazentin

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2014-10-13

Examining board members

João Luis Garcia Rosa; Ricardo Ribeiro Gudwin; Roseli Aparecida Francelin Romero

• Advisor: João Luis Garcia Rosa

Abstract

This dissertation aims to examine the K-sets, a hierarchy of biologically plausible neural networks, and apply them to the problem of motor imagery classification through electroencephalogram (EEG). Motor imagery is the act of processing a motor movement from long-term to short-term memory. Motor imagery leaves a trail in the EEG signal, which makes possible the identification and classification of different motor movements. Motor imagery classification is a complex problem due to non-linearity of the EEG time series, low signal-to-noise ratio, and the small amount of data typically available for learning. K-sets are a connectionist model that simulates the dynamic and chaotic behavior of populations of neurons in the brain, modeled based on observations of the olfactory system by Walter Freeman. K-sets have already been used in several different classification domains, including EEG, showing good results. Due to the characteristics of motor imagery classification, a hypothesis that the application of K-sets in the task could provide good results was raised. A simulator for K-sets was created for the experiments. Unfortunately, the hypothesis could not be validated, as the results of the conducted experiments with K-sets and motor imagery showed no significant improvements in comparison in the task performed. (AU)

• FAPESP's process: 12/15178-7 - Continuous electroencephalogram classification using a Connectionist Model based on Populations of Neurons
• Grantee: Denis Renato de Moraes Piazentin
• Support Opportunities: Scholarships in Brazil - Master