Decoding the dynamics of brain activity underlying conscious behavior is one of the key questions in systems neuroscience. Sensory neurons, such as those in the auditory system, can undergo rapid and task-dependent changes in their response characteristics during attentive behavior, and thereby result in functional changes in the system over time. In order to quantify human’s conscious experience, neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used to record the neural activity from the brain with millisecond temporal resolution. Therefore, a dynamic decoding framework on par with the sampling resolution of EEG/MEG is crucial in order to better understand the neural correlates underlying sophisticated cognitive functions such as attention. I will talk about two recent attempts on real-time decoding of brain neural activity during a competing auditory attention task, using Bayesian hierarchical modeling and adaptive signal processing.