EEG Knows Best: Predicting Future Performance Problems for Targeted Training

Autor:	Christine L. Belz, Charles P. R. Scott, Phan Luu, Gwendolyn E. Campbell
Rok vydání:	2011
Předmět:	medicine.diagnostic_test business.industry Computer based Filter (signal processing) Electroencephalography Machine learning computer.software_genre Task (project management) Identification (information) medicine Artificial intelligence Medical diagnosis Psychology business computer
Zdroj:	Foundations of Augmented Cognition. Directing the Future of Adaptive Systems ISBN: 9783642218514 HCI (20)
DOI:	10.1007/978-3-642-21852-1_16
Popis:	Many uses for neurophysiological data in training have been proposed in the literature [6], [10], and [11]. However, to date it has not been demonstrated that the use of EEG yields performance diagnoses that are actually more accurate. The current study investigated the capability of EEG to accurately diagnose performance difficulties by examining the predictive ability of an accurate diagnosis on future performance. The data from this study suggests that using EEG to filter a trainee's performance data prior to analysis on a computer based tank identification task yields a more accurate diagnosis than analyzing the data with the traditional statistical methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4e7fe9ecdf6a3027d7e7d39a3a18bbd8 https://doi.org/10.1007/978-3-642-21852-1_16 Zobrazit plný text záznamu