EEG motor imagery algorithms and hardware for brain control systems
EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review
Machine Learning
Summary
People with motor disabilities can use brain-computer interfaces (BCIs) to control devices just by imagining movements. This paper reviews the best ways to interpret brain signals measured by EEG devices when someone imagines moving. The authors focus on how AI methods like machine learning and deep learning help improve accuracy and speed. They also describe recent hardware advances like wearable tech and chips that work well with these AI methods, which could make BCIs more practical in daily life. The paper aims to guide others working on better brain control technologies.
What this means in practice
- •For medical device engineers: Design EEG-based systems that decode motor imagery more accurately using AI-enhanced algorithms and specialized hardware for neurorehabilitation use.
- •For wearable technology developers: Integrate compact and low-power EEG signal processors into wearable devices to enable real-time brain control applications.
A survey. It maps existing work.
Authors
Mohammad Hossein Koohi Ghamsari, Seyede Fatemeh Ghamkhari, Siavash Bayat, Ahmed Hemani
Abstract
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.