Motor Imagery Tasks Based Electroencephalogram Signals Classification Using Data-Driven Features
Brain-Computer Interface (BCI) system consist of a variety of different applications based on the processing of electroencephalograph (EEG). One of the most significant categories are based on EEG signals segmentation for “Motor Imagery” (MI) classification.When analytic methods use a fixed set of b...
Main Authors: | Vikram Singh Kardam, Sachin Taran, Anukul Pandey |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-06-01
|
Series: | Neuroscience Informatics |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528623000134 |
Similar Items
-
Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
by: Jing-Shan Huang, et al.
Published: (2021-11-01) -
A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface
by: Jing Jin, et al.
Published: (2022-01-01) -
Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification
by: Xinghe Xie, et al.
Published: (2024-01-01) -
Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks
by: Ghadir Ali Altuwaijri, et al.
Published: (2022-07-01) -
Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
by: Jun Yang, et al.
Published: (2021-11-01)