Table 2.

Discriminant function analysis: EEG versus surrogate: nonlinear measures model (jackknifed classification matrix)

	EEG (n = 32)	Surrogate (n = 32)	Total correctly classified (%)
Number classified as EEG	28	0
Number classified as surrogate	4	32
Correctly classified (%)	88	100	94

A discriminant function analysis was performed to determine if a combination of nonlinear measures could describe most of the variance in nonlinear structure in the EEG. The model selected: slope asymmetry, Kaplan’s δ ε slope at embedding 16, Kaplan’s δ ε slope at embedding 4, and 1% radius at embedding dimension m = 4 as the best combination to discriminate EEG from its surrogates. The classification matrix is provided in this figure; the model was found to be significant (Wilks λ F = 32.2, df = 1.59;p < 0.00001).