Comparison of Time-Variant Coherence Algorithms in Single-Trial: A Dynamic Analysis

Synchronization in the electroencephalogram, which can be quantified by time-variant coherence measures, reveals a communication between brain regions reflecting different functions (e.g. learning or memory). Aim of this work is to compare three time-variant coherence algorithms in single-trial. The first algorithm is based on the short-time Fourier transform, the second one on the adaptive discrete Fourier transform, whereas the third algorithm is based on a recursive smoothed pseudo Wigner-distribution. Parameters which describe the dynamic properties of the algorithms were calculated and compared on simulated data. Differences were observed in the estimation performance of the algorithms. Based on the obtained results, the algorithms are discussed for their utilization in future applications