mlsanityData to 14 Oct 2026

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

arXiv 2609.32930 ↗First posted 26 Sep 2026eess.SP cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices. Among various BCI paradigms, EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing algorithms developed over the past decade for decoding brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance EEG-based MI-BCI systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 324 references that are in the map. First posted on arXiv 26 Sep 2026.

Builds on

15 of 324 references.

References · 15

  1. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  2. 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 199
  3. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  4. 2018Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approachcited by 76
  5. 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 59
  6. 2020Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applicationscited by 34
  7. 2020An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computingcited by 16
  8. 2020GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signalscited by 16
  9. 2021MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classificationcited by 16
  10. 2020Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfacescited by 8
  11. 2019Early Prediction of Epilepsy Seizures VLSI BCI Systemcited by 2
  12. 2020Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patientscited by 2
3 more
  1. 2025Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Modelscited by 2
  2. 2022An approach to interfacing the brain with quantum computers: practical steps and caveatscited by 1
  3. 2023Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Executioncited by 1

Built on by

Not cited yet.