iPhoneAlle anzeigen
iPhone 11iPhone 11 ProiPhone 11 Pro MaxiPhone 12iPhone 12 ProiPhone 12 Pro MaxiPhone 12 miniiPhone 13iPhone 13 ProiPhone 13 Pro MaxiPhone 13 miniiPhone 14iPhone 14 PlusiPhone 14 ProiPhone 14 Pro MaxiPhone 15iPhone 15 PlusiPhone 15 ProiPhone 15 Pro MaxiPhone 16iPhone 16 PlusiPhone 16 ProiPhone 16 Pro MaxiPhone 16eiPhone 8iPhone 8 PlusiPhone SE 2020iPhone SE 2022iPhone XiPhone XRiPhone XSiPhone XS Max
Galaxy A-SerieAlle anzeigen
Galaxy A12Galaxy A13Galaxy A13 5GGalaxy A14Galaxy A15Galaxy A16Galaxy A17 5GGalaxy A20eGalaxy A21Galaxy A22Galaxy A23Galaxy A24Galaxy A25Galaxy A26 5GGalaxy A32Galaxy A33Galaxy A34Galaxy A35Galaxy A36 5GGalaxy A40Galaxy A41Galaxy A42Galaxy A50Galaxy A51Galaxy A52Galaxy A53Galaxy A54Galaxy A55Galaxy A56 5GGalaxy A70Galaxy A71Galaxy A72
Redmi SeriesAlle anzeigen
Redmi 10Redmi 13CRedmi 15 5GRedmi Note 10Redmi Note 10 ProRedmi Note 11Redmi Note 11 ProRedmi Note 11 Pro Plus 5GRedmi Note 11sRedmi Note 12Redmi Note 12 5GRedmi Note 12 ProRedmi Note 12 Pro 5GRedmi Note 12 Pro PlusRedmi Note 13Redmi Note 13 5GRedmi Note 13 ProRedmi Note 13 Pro PlusRedmi Note 14Redmi Note 14 Pro PlusRedmi Note 8Redmi Note 8 ProRedmi Note 9Redmi Note 9 Pro
- Startseite
Bücher Englische Bücher Wissen & Bildung Naturwissenschaft, Medizin, Informatik, Technik Technik Elektronik, Elektro- und Nachrichtentechnik Cerebral Evoked Potential Estimation
Derzeit nicht verfügbar
Handgeprüfte Gebrauchtware
Bis zu 50 % günstiger als neu
Der Umwelt zuliebe
Cerebral Evoked Potential Estimation
Jianhua Zhang (Unbekannter Einband, Englisch)
★★★★★
☆☆☆☆☆ Keine Bewertungen vorhanden
Optischer Zustand
Beschreibung
The cerebral evoked potentials (EPs) are electrical responses of the central nervous system (CNS) to sensory stimuli presented in a well-controlled paradigm. EPs are deeply buried in the spontaneous electroencephalogram (EEG) background activity, whose signal-to-noise-ratio (SNR) is typically less than 0dB (even lower than -60dB sometimes). The nature of poor SNR makes the EP estimation a… formidable problem in the past decades in the field of EEG/EP research. It is necessary and of interest to develop more powerful techniques for reliably improving the SNR with much reduced number of trials. The new theoretical contributions of the work are summarized as follows.
A. Due to their multi-resolution analysis (MRA) capacity, wavelet transform and wavelet packet transform methods are systematically explored for estimation and quantitative analysis of the EPs. Mallat’s pyramidal algorithm is used to derive three wavelets and wavelet-packets based de-noising approaches for estimating EP signal in the joint time-frequency domain.
B. The regularization network with an efficient adaptive regularization training rule based on the steepest gradient-descent searching procedure is proposed and then successfully applied to blind EP estimation.
C. A fast algorithm is proposed for optimizing the linear and nonlinear parameters in a general adaptive fuzzy dynamical system, i.e., nonlinear adaptive fuzzy approximator (NAFA). The NAFA is shown to be robust against the noise contaminating the I/O training data and hence applicable to single-trial EP estimation problem, which rejects most parametric model based filtering (or estimation) methods. In particular, we proposed a fast training method by combining Kalman filter and LMS learning for the NAFA model. As far as we know, the proposed NAFA technique is the only published work in which an adaptive fuzzy system is used for the estimation of EPs in a single trial.
The simulations and experiments are performed using simulated and clinical data of visual evoked potentials recorded with a standard recording instrumentation. The feasibility and effectiveness of the time-frequency analysis and intelligent data modeling methods proposed are validated by the simulation and experimental results. The novel EP estimation methods can be used for unambiguous clinical diagnostics. Dieses Produkt haben wir gerade leider nicht auf Lager.
Handgeprüfte Gebrauchtware
Bis zu 50 % günstiger als neu
Der Umwelt zuliebe
Technische Daten
Erscheinungsdatum
28.04.2005
Sprache
Englisch
EAN
9783898259743
Herausgeber
Winter Industries
Serien- oder Bandtitel
Dissertation Classic
Sonderedition
Nein
Autor
Jianhua Zhang
Seitenanzahl
172
Auflage
1
Einbandart
Unbekannter Einband
-.-
★★★★★
☆☆☆☆☆ Leider noch keine Bewertungen
Leider noch keine Bewertungen
Sicher bei rebuy kaufen
Schreib die erste Bewertung für dieses Produkt!
Wenn du eine Bewertung für dieses Produkt schreibst, hilfst du allen Kund:innen, die noch überlegen, ob sie das Produkt kaufen wollen. Vielen Dank, dass du mitmachst!
Sicher bei rebuy kaufen