Motor Imagery

58 papers with code • 0 benchmarks • 0 datasets

Classification of examples recorded under the Motor Imagery paradigm, as part of Brain-Computer Interfaces (BCI).

A number of motor imagery datasets can be downloaded using the MOABB library: motor imagery datasets list


Use these libraries to find Motor Imagery models and implementations

Most implemented papers

EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

vlawhern/arl-eegmodels 23 Nov 2016

We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.

Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework

miaozhengqing/lmda-code 19 Feb 2022

Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

xiangzhang1015/Brain_typing 26 Sep 2017

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

MultiScale-BCI/IV-2a 18 Jun 2018

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.

Physics-inform attention temporal convolutional network for EEG-based motor imagery classification

Altaheri/EEG-ATCNet IEEE Transactions on Industrial Informatics 2022

In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability

miaozhengqing/lmda-code 29 Mar 2023

By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks.

Time-space-frequency feature Fusion for 3-channel motor imagery classification

miaozhengqing/lmda-code 4 Apr 2023

TSFF-Net comprises four main components: time-frequency representation, time-frequency feature extraction, time-space feature extraction, and feature fusion and classification.

EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms

martinwimpff/channel-attention 17 Oct 2023

The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding.

Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI

musicalka/fwr-csp IEEE Signal Processing Letters 2018

Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other.

Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

mcd4874/neurips_competition 8 Aug 2018

Our approach has three desirable properties: 1) it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction and machine learning algorithms can then be applied to the aligned trials; 2) its computational cost is very low; and, 3) it is unsupervised and does not need any label information from the new subject.