Literature DB >> 15876641

A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier.

Reza Boostani1, Mohammad Hassan Moradi.   

Abstract

High rate classification of imagery tasks is still one of the hot topics among the brain computer interface (BCI) groups. In order to improve this rate, a new approach based on fractal dimension as feature and Adaboost as classifier is presented for five subjects in this paper. To have a comparison, features such as band power, Hjorth parameters along with LDA classifier have been taken into account. Fractal dimension as a feature with Adaboost and LDA can be considered as alternative combinations for BCI applications.

Mesh:

Year:  2004        PMID: 15876641     DOI: 10.1088/1741-2560/1/4/004

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  8 in total

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Review 3.  Progress in EEG-Based Brain Robot Interaction Systems.

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Journal:  J Biomed Phys Eng       Date:  2022-04-01

6.  Channel selection and feature projection for cognitive load estimation using ambulatory EEG.

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Journal:  Comput Intell Neurosci       Date:  2007

7.  A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface.

Authors:  Francesco Cavrini; Luigi Bianchi; Lucia Rita Quitadamo; Giovanni Saggio
Journal:  Comput Intell Neurosci       Date:  2015-12-24

8.  Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection.

Authors:  Yi-Hung Liu; Shiuan Huang; Yi-De Huang
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

  8 in total

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