Detection of spatiotemporal phase patterns in ECoG using adaptive mixture models

Phase cone detection optimization in EEG data

“…before a stimulus is applied. A decrease in cone activity has been observed at time around 500 ms after stimulation [9]. This effect has been described in large chaotic systems that reflect a build-up, preceding the 'avalanche-effect'.…”

“…Previous results indicated a decrease in during the poststimulus period [9]. This may an increased synchronization as part of the manifesting the construction of meaning sensory data: the "aha" effect.…”

“…Following [9], a mixture model was designed to process a series of 8 by 8 frames and detect the occurrence of the cones over time. Given a set of H models that depend on a set of parameters S={Sh,h=1..H} and a set of data inputs X ={xn,n=1..N}, the maximum likelihood estimate of the parameters is obtained by maximizing the following objective function:…”

“…Then we utilize the corresponding DL equations applicable to the case of time varying EEG [7], [8]. We describe the algorithm to solve these equations and estimate the model parameters [9]. We analyze in detail the frequency-dependence of the phase cones.…”