Classification of the four‐class motor imagery signals using continuous wavelet transform filter bank‐based two‐dimensional images

2021 ◽  
Author(s):  
Rupesh Mahamune ◽  
Shahedul H. Laskar
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Filter Bank ◽  
Continuous Wavelet ◽  
Two Dimensional ◽  
Two Dimensional Images

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

scholarly journals Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1199 ◽  
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Continuous Wavelet ◽  
Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

Spatial carrier fringe pattern demodulation by use of a two-dimensional continuous wavelet transform

2006 ◽  
Vol 45 (34) ◽  
pp. 8722 ◽  
Author(s):  
Munther A. Gdeisat ◽  
David R. Burton ◽  
Michael J. Lalor
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Fringe Pattern ◽  
Continuous Wavelet ◽  
Two Dimensional
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