@article{3097080,
    title = "Design and implementation of a multi-PNN structure for discriminating
one-month abstinent heroin addicts from healthy controls using the P600
component of ERP signals",
    author = "Kalatzis, I and Piliouras, K and Ventouras, E and Papageorgiou, CC and and Liappas, IA and Nikolaou, CC and Rabavilas, AD and Cavouras, DD",
    journal = "Pattern Recognition Letters",
    year = "2005",
    volume = "26",
    number = "11",
    pages = "1691-1700",
    publisher = "ELSEVIER SCIENCE BV",
    issn = "0167-8655",
    doi = "10.1016/j.patrec.2005.01.012",
    keywords = "heroin addicts; event-related potentials (ERPs); P600 component; pattern
recognition",
    abstract = "A multi-probabilistic neural network (multi-PNN) classification
structure has been designed for distinguishing one-month abstinent
heroin addicts from normal controls by means of the Event-Related
Potentials’ P600 component, selected at 15 scalp leads, elicited under a
Working Memory (WM) test. The multi-PNN structure consisted of 15
optimally designed PNN lead-classifiers feeding an end-stage PNN
classifier. The multi-PNN structure classified correctly all subjects.
When leads were grouped into compartments, highest accuracies were
achieved at the frontal (91.7%) and left temporo-central region
(86.1%). Highest single-lead precision (86.1%) was found at the P3, C5
and F3 leads. These findings indicate that cognitive function, as
represented by P600 during a WM task and explored by the PNN signal
processing techniques, may be involved in short-term abstinent heroin
addicts. Additionally, these findings indicate that these techniques may
significantly facilitate computer-aided analysis of ERPs. (c) 2005
Elsevier B.V. All rights reserved."
}