Decomposition Frequency Optimization in wavelet-Based Template Matching Algorithms to Manage P300 Latency Jitter

Event Related Potentials (ERPs) are modifications of the brain activity in response to external sensory stimulation. P300 is a positive ERP component occurring 300 ms after the presentation of a rare stimulus and indicating the conscious perception of an unexpected change in sensory stimulation. Since ERPs amplitude is several factors inferior to the magnitude of background electroencephalography, the most diffused approach to render ERPs correctly detectable is time-domain averaging. ERPs are physiologically affected by variation in latency, referred to as latency jitter. The most promising techniques to address latency jitter are cross-correlation template matching algorithms, that estimate single trial latencies and resynchronize them according to a template that best describes the ERP component. Adaptive Wavelet Filtering (AWF) is an algorithm applied in the wavelet domain to supply the optimal compromise of time-frequency resolution in the detection of the ERP. The choice of an appropriate frequency of analysis is fundamental in the application of AWF, since it is crucial to identify the correct template and to estimate the single trial latency. In this study, we investigated the influence of the frequency in the performance of AWF in order to optimize it. The algorithm was tested both for data simulated varying the entity of latency shift and signal to noise ratio and for data recorded from 11 healthy subjects during an auditory oddball paradigm.