Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns

2004 ◽  
Vol 115 (12) ◽  
pp. 2744-2753 ◽  
Author(s):  
Tao Wang ◽  
Jie Deng ◽  
Bin He
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Time Frequency

scholarly journals Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification

2021 ◽  
pp. 1-1
Author(s):  
Yangyang Miao ◽  
Jing Jin ◽  
Ian Daly ◽  
Cili Zuo ◽  
Xingyu Wang ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Time Frequency ◽  
Common Time

scholarly journals Common Spatio-Time-Frequency Patterns for Motor Imagery-Based Brain Machine Interfaces

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hiroshi Higashi ◽  
Toshihisa Tanaka
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Time Windows ◽  
Natural Extension ◽  
Time Frequency ◽  
Spatial Weights ◽  
Time Patterns ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
Efficient Decoding

For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI.

Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

2021 ◽  
Author(s):  
Liangsheng Zheng ◽  
Yue Ma ◽  
Mengyao Li ◽  
Yang Xiao ◽  
Wei Feng ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Motor Imagery ◽  
Eeg Classification ◽  
Time Frequency ◽  
Frequency Decomposition

scholarly journals Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6084-6093 ◽  
Author(s):  
Baoguo Xu ◽  
Linlin Zhang ◽  
Aiguo Song ◽  
Changcheng Wu ◽  
Wenlong Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Convolutional Network ◽  
Eeg Classification ◽  
Time Frequency

scholarly journals Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

2020 ◽  
Vol 14 ◽  
Author(s):  
Diego Collazos-Huertas ◽  
Julian Caicedo-Acosta ◽  
German A. Castaño-Duque ◽  
Carlos D. Acosta-Medina
Keyword(s):  
Motor Imagery ◽  
Time Frequency

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.

scholarly journals Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

2019 ◽  
Vol 49 (9) ◽  
pp. 3322-3332 ◽  
Author(s):  
Yu Zhang ◽  
Chang S. Nam ◽  
Guoxu Zhou ◽  
Jing Jin ◽  
Xingyu Wang ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns
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