# Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network

arXiv: https://arxiv.org/abs/1908.02252 · HTML: https://mlsanity.com/paper/1908.02252/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 12 of 12)

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- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)
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- [Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2107.13505/) (2021)
- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [Partial Label Learning for Emotion Recognition from EEG](https://mlsanity.com/paper/2302.13170/) (2023)
- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis](https://mlsanity.com/paper/2107.08514/) (2021)
- [Spatio-temporal Gait Feature with Global Distance Alignment](https://mlsanity.com/paper/2203.03376/) (2022)
- [EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis](https://mlsanity.com/paper/2308.13371/) (2023)
- [Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach](https://mlsanity.com/paper/2607.01795/) (2026)

## Abstract (arXiv)

Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications. This paper proposes a novel solution for classification of left/right hand movement by exploiting a Long Short-Term Memory (LSTM) network with attention mechanism to learn the electroencephalogram (EEG) time-series information. To this end, a wide range of time and frequency domain features are extracted from the EEG signals and used to train an LSTM network to perform the classification task. We conduct extensive experiments with the EEG Movement dataset and show that our proposed solution our method achieves improvements over several benchmarks and state-of-the-art methods in both intra-subject and cross-subject validation schemes. Moreover, we utilize the proposed framework to analyze the information as received by the sensors and monitor the activated regions of the brain by tracking EEG topography throughout the experiments.