Analysis of Event-Related Potentials Elicited During Speaker Recognition Tasks

1. Analysis of Event-Related Potentials Elicited During Speaker Recognition Tasks Dror Lederman1, Joseph Tabrikian1, Hillel Pratt2 (1) Ben-Gurion University of the Negev and (2) Technion- Israel Institute of Technology • Research questions: • What information concerning • identification of different speakers • can be extracted from the EEG • measurements? Is it possible to • perform speaker identification (in the • general sense) based on EEG • measurements? • Which brain areas are responsible for • identification of speakers? • What is the spatio-temporal course of • the physiological processes • associated with speaker • identification? • Problems/tasks: • Decomposition of the ERPs from the • noisy EEG measurements. • Multi-channel classification of the • ERP signals. • Estimation and localization of the • ERPs' intra-cranial sources. • Primary research objective: • Develop statistical model-based (GMM/CD-HMMs) methods for ERPs estimation and classification and utilize these methods to answer the major research questions. • Secondary researchobjective: • Investigate the spatio-temporal course of the processes that occur in the brain during speaker recognition tasks based on ERPs source localization (LORETA). Model-based approaches for ERP estimation Multi-channel ERP Classification 1. HMM-based ERP averaging Preliminary classification results Classification of two ERP classes elicited during imageryleft/right hand movements tasks (2nd BCI competition) 2. Parallel Model Combination • Research plan • Acquisition of speaker identification • AERPs databases. Three • databases will be acquired: • Speaker gender identification AERPs. • Familiar/unfamiliar speaker identification AERPs. • Speaker identification AERPs. • ERP estimation methods • development. The methods will be • evaluated using: • Synthesized DB. • AERPs speaker recognition DB. • ERP classification methods • development and evaluation. • Spatio-temporal course investigation • (LORETA) . • Preliminary work conclusions • The CD-HMM-based approach outperforms other acceptable approaches. • The research has the potential to contribute to the improvement of ERPs processing methods in general, to provide additional insight into the speaker recognition cognitive process and take the BCI research one step forward. • The approaches presented here utilize a-priori statistical information on the background EEG and ERP signals and the statistical properties of these signals. Consequently, they are expected to outperform current ERP estimation methods. 3. Stationary-nonstationary decomposition Let the vectors , and (each of size ) represent the ongoing EEG, the ERP and the noisy EEG measurements, respectively, such that . Since the zero-mean background EEG is presumed to be WSS, its covariance matrix, , , is Toeplitz. Therefore: The solutions can be found using Lagrange-multipliers. This method is an extension to the Minimum Mean Square Error approach with the EEG stationarity constraints. In memory of Prof. Arnon Cohen who initiated this project Prof. ARNON COHEN 5.02.1938 - 19.02.2005