Time-Frequency Analysis of Electroencephalograph (EEG) for Feature Optimization

Lingua: inglese

Editore: GRIN Verlag Feb 2022, 2022

3346584399 / 9783346584397

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Visualizza gli articoli di questo venditore
Brossura

Condizione: Nuovo

EUR 52,95

EUR 60,00 spedizione 
Spedito da Germania a U.S.A.

Quantità: 1 disponibili

Aggiungi al carrello
Resi gratuiti per 30 giorni

Descrizione dell’articolo da parte del venditore

This item is printed on demand - Print on Demand Titel. Neuware -Document from the year 2017 in the subject Medicine - Biomedical Engineering, grade: 3.0, , language: English, abstract: Electroencephalograph (EEG) has been widely used for BCI applications due to its non-invasiveness, ease of implementation, and cost-efficiency. The collected EEG signal is non-stationary and has task-related information buried in the frequency and temporal domains. In this book, we focused on developing time-frequency decomposition methods for improving the feature extraction module in BCI systems. The obtained features are then optimized by identifying subject-specific reactive band and employing evolutionary algorithm-based methods for optimizing the obtained features, improving the BCI systems' performance. A signal model named as band-limited multiple Fourier linear combiner (BMFLC) is employed to model the non-stationarity in the EEG signal for feature extraction. The non-stationary amplitude oscillation is presented as adaptive weights in the model and estimated with various adaptive filters such as least-mean square (LMS), Kalman filter (KF), or Kalman Smoother (KS). The estimated coefficients serve as features for classification. Our results suggest that the BMFLC-LMS, BMFLC-KF, and BMFLC-KS are all sufficient in modeling EEG signal in the band with average estimation accuracies of 93%, 99%, and 98%, respectively. We modelled motion-induced EEG signal in frequency domain. We found that most subjects present a subject- specific reactive band during motion tasks. We then constructed features for a classifier that used only the frequency information in the subject-specific reactive band. As a result, the classification accuracy of the BCI system is improved compared to the system which uses the complete band information. Features obtained from multiple EEG channels need to be optimized to enhance the performance of the BCI systems. Essentially, two problems need to be resolved: 1) volume conduction; 2) dimensionality. The volume conduction can be mitigated if spatial filter is employed, and the dimension of the feature vector can be reduced if a feature selection procedure is adopted. An evolutionary algorithm (EA) based approach was developed to estimate spatial filter and reduce feature dimension simultaneously. We show that the BMFLC-KF combined with the evolutionary al- algorithm has the highest classification accuracy compared to other BMFLC-KF based approaches and is superior to the traditionally employed band-power methods.GRIN Publishing GmbH, Waltherstraße 23, 80337 München 168 pp. Englisch.

Codice articolo 9783346584397

Titolo
Time-Frequency Analysis of Electroencephalograph (EEG) for Feature Optimization
Autore
Kalyana Veluvolu
Editore
GRIN Verlag Feb 2022
Anno di pubblicazione
2022
Condizione
Neu
Rilegatura
Taschenbuch
Lingua
inglese
ISBN 10
3346584399
ISBN 13
9783346584397
Peso dell'articolo
253 grammi
Dimensioni
210x148x12 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 60 a 60 giorni lavorativiDa 60 a 60 giorni lavorativi
Primo articoloEUR 60,00EUR 75,00
I tempi di consegna sono stabiliti dai venditori e variano in base al corriere e al paese. Gli ordini che devono attraversare una dogana possono subire ritardi e spetta agli acquirenti pagare eventuali tariffe o dazi associati. I venditori possono contattarti in merito ad addebiti aggiuntivi dovuti a eventuali maggiorazioni dei costi di spedizione dei tuoi articoli.

Metodi di pagamento

  • Visa
  • Mastercard
  • American Express
  • Carte Bleue
  • Apple Pay
  • Google Pay
  • Assegno
  • PayPal

Descrizione dello Store

Impressum Thorsten Retsch Buchversand Mimpf2000 Oberölschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir führen gebrauchte Bücher aus allen Sparten der Literatur

Specializzazione

Modernes Antiquariat - Bücher von 1960 bis heute

Informazioni sull’azienda del venditore

buchversandmimpf2000

Germania