# Use of Artificial Intelligence in the Classification of Upper-Limb Motion Using EEG and EMG Signals: A Review

## TL;DR

This review explores how AI, especially deep learning, is used to classify upper-limb movements using EEG and EMG signals, showing a shift from traditional methods to newer architectures.

## Contribution

The paper provides a systematic review of AI applications in motion classification, highlighting the transition from traditional to deep learning methods.

## Key findings

- Deep learning models like CNNs, LSTMs, and Transformers are increasingly used for motion classification.
- Traditional models like LDA and SVMs remain relevant due to their efficiency and robustness.
- Most studies rely on EEG-only data, with limited use of hybrid EEG-EMG systems.

## Abstract

This systematic review summarizes the application of artificial intelligence (AI) in classifying upper-limb motion using Electroencephalogram (EEG) and Electromyogram (EMG) signals, focusing on the field’s progression from Traditional Machine Learning (TML) to Deep Learning (DL) architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a search of PubMed, IEEEXplore, and Web of Science yielded 301 eligible studies published up to June 2025. The results indicate a change from classical classifiers like Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) toward DL approaches. While Convolutional Neural Networks (CNNs) remain the most frequently implemented, emerging architectures, including Long Short-Term Memory (LSTM) networks and Transformers, have demonstrated remarkable performance. Despite the rise of DL, classical models remain highly relevant due to their robustness and efficiency. This review also identifies a heavy reliance on EEG-only modalities (60%), with only 7% of studies utilizing hybrid EEG-EMG systems, representing a potential missed opportunity for signal fusion.