Algorithm 2 Self relative evaluation

Input    data: EEG signals of all subjects available in the dataset.    P: parametric representation of the designed EEG-based biometric system.    $S_1={\left\{O_i\right\}}_{i=1}^m$: range of filter orders.    $S_2={\left\{f_j\right\}}_{j=1}^r$: range of frequency sub-bands.    S: openness simulation model.    update: the class-update strategy.   Output    $O{A}_m={\left\{o{a}_{m_k}\right\}}_{k=1}^{m*r}$: The mean vector of openness accuracy per each distribution of identity information.    $metric={\left\{m_k\right\}}_{k=1}^{m*r}$: The self-relative evaluation metrics.     START 1: $D={\left\{D^k\right\}}_{k=1}^{m*r}\leftarrow$identity-information-distribution $(data,P,S_1,S_2)$   2: For k=1, ...m*r 3:   $o{a}_{m_k}\leftarrow$ openness-condition $(D^k,S,update)$ 4:   $m_k\leftarrow$ performance-evaluation $\left(o{a}_{m_k}\right)$   END