Estimation of the position of electrocortical generators via subspace techniques

There are a number of approaches to the application of subspace techniques for solving spectral estimation problems. These approaches are derived from the covariance matrix which is constructed from incoming data. The covariance matrix can be broken down through the use of appropriate matrix properties and eigen-decomposition techniques into two subspaces. The performance of three traditional algorithms which incorporate subspace techniques in direction of arrival are evaluated under both white and 1/f noise conditions. 1/f noise is chosen because it is typical of the EEG signals. Simulation results suggest that the Johnson and DeGraaf (1982) direction finding algorithm performs best under both noise environments. A typical sample of EEG data was used to evaluate the performance of the three algorithms. The Johnson and DeGraaf algorithm gives estimates for the direction of the signal which approximately agree with the anatomical locations of possible electrocortical generators