$Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface$

Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification acc...

Bibliografske podrobnosti
Main Authors:	Loo, C.K., Samraj, A., Lee, G.C.
Format:	Article
Jezik:	English
Izdano:	2011
Teme:	T Technology (General)
Online dostop:	http://eprints.um.edu.my/5182/1/Evaluation_of_Methods_for_Estimating.pdf

Podobne knjige/članki

Motor imaginary-based brain machine interface design using programmable logic controllers for the disabled
od: Jeyabalan, V., et al.
Izdano: (2010)

Relevance-based channel selection in motor imagery brain-computer interface
od: Nagarajan, Aarthy, et al.
Izdano: (2023)

Fractal dimension methods to determine optimum EEG electrode placement for concentration estimation
od: Siamaknejad, Hossein, et al.
Izdano: (2019)

Efficient EEG frequency band selection techniques for a robust motor imagery based brain-computer interface
od: Kavitha P. Thomas
Izdano: (2011)

Motor imaginary signal classification using adaptive recursive bandpass filter and adaptive autoregressive models for brain machine interface designs
od: Jeyabalan, V., et al.
Izdano: (2008)

Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces
od: Sadiq, Muhammad Tariq, et al.
Izdano: (2021)

The fractal dimension of an oil spray
od: Castrejón García, R, et al.
Izdano: (2003)

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface
od: Nagarajan, Aarthy, et al.
Izdano: (2024)

Brain functional changes in stroke following rehabilitation using brain-computer interface-assisted motor imagery with and without tDCS: a pilot study
od: Hu, Mengjiao, et al.
Izdano: (2022)

Brain computer interface for post-stroke motor rehabilitation
od: Mane, Ravikiran Tanaji
Izdano: (2021)

Signal processing techniques for motor control brain computer interface systems
od: Neethu Robinson
Izdano: (2015)

A generalized fractal dimension as an image feature
od: Al-Saidi, Nadia Mohammed Ghanim, et al.
Izdano: (2015)

An improved fuzzy fractal dimension for texture analysis
od: Al-Saidi, Nadia Mohammed Ghanim, et al.
Izdano: (2015)

Dimension results and local times for superdiffusions on fractals
od: Hambly, BM, et al.
Izdano: (2023)

Using transcranial direct current stimulation to augment the effect of motor imagery-assisted brain-computer interface training in chronic stroke patients—cortical reorganization considerations
od: Chew, Effie, et al.
Izdano: (2020)

Evaluation of some composite paint coatings' appearance quality using fractal dimension
od: Loganina, Valentina, et al.
Izdano: (2023)

An integrated approach for head gesture based interface
od: Lee, G.C., et al.
Izdano: (2012)

Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations
od: Lau, Zen Juen, et al.
Izdano: (2022)

Box-covering algorithm for fractal dimension of complex networks
od: Schneider, Christian Michael, et al.
Izdano: (2012)

Motor imagery impairment in postacute stroke patients
od: Braun, N, et al.
Izdano: (2017)

Effects of an orientation illusion on motor performance and motor imagery.
od: Glover, S, et al.
Izdano: (2005)

Fractal Dimension as an Indication of the Terminating Runtime of Discrete Dynamical Systems
od: Zenil, F
Izdano: (2011)

Asymptotics for the spectral and walk dimension as fractals approach Euclidean space
od: Hambly, B, et al.
Izdano: (2002)

Advanced brain computer interface
od: Arvaneh, Mahnaz
Izdano: (2014)

Neural signal processing techniques to analyse motor functions in EEG-based brain-computer interfaces
od: Vikram, Shenoy Handiru
Izdano: (2018)

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory
od: Hamedi, Mahyar
Izdano: (2023)

Fractal Characteristics of Newton's Method on Polynomials
od: Drexler, M, et al.
Izdano: (1996)

Random fractal strings: their zeta functions, complex dimensions and spectral asymptotics
od: Hambly, B, et al.
Izdano: (2003)

Random fractal strings: Their zeta functions, complex dimensions and spectral asymptotics
od: Hambly, B, et al.
Izdano: (2006)

Motor imagery classification based on deep learning
od: Geng, Zhiheng
Izdano: (2023)

Modern spectral estimation methods applied to FOPEN SAR imagery
od: Kang, Leiter, 1978-
Izdano: (2014)

Fractal estimation using models on multiscale trees
Izdano: (2003)

Brain-computer interface and visual perception
od: Cheoh, Xin Mei
Izdano: (2015)

Brain-computer interface and visual perception
od: Chong, Ming Jie
Izdano: (2014)

Brain-computer interface for mental attention
od: Fatemeh Fahimi
Izdano: (2020)

Brain computer interface for analyzing emotions
od: Tang, Cheng
Izdano: (2017)

Roles of Supplementary Motor Areas in Auditory Processing and Auditory Imagery.
od: Lima, C, et al.
Izdano: (2016)

Technology integration methods for bi-directional brain-computer interfaces and XR-based interventions
od: Landin, K, et al.
Izdano: (2020)

Fractal dimension algorithm for detecting oil spills using RADARSAT-1 SAR
od: Marghany, Maged, et al.
Izdano: (2007)

Assessment of fractal dimension and geometrical characteristics of the landslides identified in North of Tehran, Iran
od: Pourghasemi, Hamid Reza, et al.
Izdano: (2014)