Electroencephalographic imaging and biofeedback training using Z-scores: databases and LORETA-based methods
- Thomas F. Collura
- Andre W. Keizer
- Rubén Pérez-Elvira
- Steven Warner
- Thomas Feiner
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Universidad Pontificia de Salamanca
info
- Dan R. Chartier (ed. lit.)
- Mary Blair Dellinger (ed. lit.)
- James R. Evans (ed. lit.)
- Helen Kogan Budzynski (ed. lit.)
Editorial: Elsevier Science & Technology
ISBN: 978-0-323-89827-0
Año de publicación: 2023
Páginas: 35-61
Tipo: Capítulo de Libro
Resumen
This chapter provides updated material on the technical background and clinical results obtained in the implementation of live Z-score-based training (LZT) in an electroencephalography (EEG) biofeedback system. The system has now been in use for over 15 years, and several types of vendor-based solutions are now in use, with a growing clinical and research presence. The LZT approach makes it possible to compute, view, and process normative Z-scores in real-time as a fundamental element of EEG biofeedback. While employing the same type of database as conventional QEEG postprocessing software, LZT software is configured to produce results in real-time, suiting it to live assessment and training, rather than solely for later analysis and review. The Z-scores described here are based upon published databases, computed in real-time by applying digital filters to the raw EEG, and computing live estimates of power and connectivity values, which are updated every 125 milliseconds. Systematic comparisons verify that the live Z-scores correspond in an expected way to static Z-scores when computed using long segments of data.