Simplification of high-dimensional chaos in EEG

Recent nonlinear analysis found evidence of high-dimensional chaos in EEG other than low dimensional one; To make feasible the application of low-dimensional chaos theories and algorithms to high dimensional system, one way is to simplify the raw EEG data by a novel approach called unbiased autocovariance analysis(UA). The dimension of UA data is reduced. In this study, the chaotic component in EEG signal was suppressed while the deterministic one was highlighted. We estimated the correlation dimension of UA signals(UAD2). First multiple Lorenz attractors were formed to test UA approach Nest UA was applied to the clinical EEG data at C3,C4 channels from patients with Infantile Spasm(IS) and controls. UA method proved to be successful in reducing dimension. The difference of UAD(2) between IS group and control group was still visible, although not as significant as that of D-2. Most importantly here, the preserved 'deterministic' component presented great difference between them. The prospect and open questions were briefly addressed.