Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded or Decoded in Single-Trial Oscillatory Brain Waves

Abstract

Even in cases when we recognize identical objects or when we behave similarly, the spatiotemporal activities in the brain are likely to fluctuate to various degrees. Temporally fluctuating responses easily decrease by averaging replicate measures. We previously developed a wavelet correlation analysis that tolerates the across-trial oscillatory phase variability observed in odor-induced cortical responses. The wavelet correlation analysis revealed a change in the neuronal information redundancy of transient and oscillatory brain waves from the dependencies on stimulus experience (high redundancy) to stimulus quality (low redundancy) between the input and output layers of the anterior piriform cortex in guinea pigs. We report on its application to estimate information in the fine temporal structures of single-trial brain waves. By using a set of standard brain waves for each information in a given category, the highest wavelet correlation coefficients provided the first candidate of estimated information with 75% accuracy. Moreover, the probability of including the correct information for the two upper candidates, regardless of information redundancy of the signal sources, was >92%. The wavelet correlation analysis is useful for similarity analyses and real-time estimates of in-brain information and for its application to brain-machine interfaces or medical/research tools.

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