Key Takeaways & Executive Findings
- •• Recent advances in neural signal processing, including deep learning, have significantly improved BCI accuracy and reliability. • Hybrid BCI systems combining multiple modalities enhance performance and user adaptability. • BCI applications extend beyond motor rehabilitation to cognitive enhancement and neurofeedback therapy. • Challenges such as signal non-stationarity and long-term stability remain, but closed-loop adaptive systems offer promising solutions.
Abstract
Brain-computer interfaces (BCIs) have emerged as a transformative technology enabling direct communication between the brain and external devices, offering unprecedented opportunities for restoring motor function in paralyzed individuals and enhancing human-computer interaction. This comprehensive review synthesizes recent advances in BCI technology, focusing on neural signal acquisition, signal processing algorithms, and diverse applications. We systematically analyze invasive and non-invasive recording modalities, including electroencephalography (EEG), electrocorticography (ECoG), and intracortical microelectrode arrays, highlighting their respective advantages and limitations. The review delves into state-of-the-art signal processing techniques, such as adaptive filtering, common spatial patterns, and deep learning-based classification, which have significantly improved the accuracy and reliability of BCI systems. Furthermore, we explore the expanding landscape of BCI applications, ranging from assistive communication and motor rehabilitation to cognitive enhancement and neurofeedback therapy. Critical challenges, including signal non-stationarity, user variability, and long-term stability, are discussed alongside emerging solutions such as hybrid BCI architectures and closed-loop adaptive systems. By integrating findings from recent studies and clinical trials, this review provides a forward-looking perspective on the future of BCI technology, emphasizing the need for interdisciplinary collaboration and translational research to bridge the gap between laboratory innovations and real-world clinical adoption. Our analysis underscores the potential of BCIs to revolutionize neurorehabilitation and human augmentation, while also addressing ethical and societal implications. This comprehensive overview serves as a valuable resource for researchers, clinicians, and engineers seeking to understand the current state and future directions of brain-computer interface technology.
1. Introduction
Brain-computer interfaces (BCIs) represent a paradigm shift in human-machine interaction, enabling direct communication pathways between the brain and external devices. Over the past two decades, BCI research has progressed from fundamental neuroscience inquiries to practical applications, driven by advances in neuroimaging, signal processing, and machine learning. The primary goal of BCI technology is to restore or augment human capabilities, particularly for individuals with severe motor disabilities, by translating neural activity into commands that control assistive devices, communication tools, or virtual environments.
This review provides a comprehensive overview of the current state of BCI technology, focusing on the critical components of signal acquisition, processing, and application. We examine both invasive and non-invasive recording techniques, discuss the evolution of signal processing algorithms, and highlight the diverse range of BCI applications that have emerged in recent years. By synthesizing findings from recent studies and clinical trials, we aim to identify key trends, challenges, and future directions in this rapidly evolving field.
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ZHANG Wei, LI Ming, WANG Fang, CHEN Jie, LIU Yang (2026). Advances in Brain-Computer Interface Technology: A Comprehensive Review of Neural Signal Processing and Applications. Journal of Shanghai Jiao Tong University (Science) (上海交通大学学报). https://doi.org/10.16183/j.cnki.jsjtu.2026.105
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Frequently Asked Questions
What is a brain-computer interface (BCI)?
A brain-computer interface (BCI) is a system that acquires brain signals, analyzes them, and translates them into commands that are relayed to an output device to carry out a desired action. It enables direct communication between the brain and an external device, bypassing normal neuromuscular pathways.
What are the main types of BCI systems?
BCI systems can be classified as invasive, semi-invasive, or non-invasive based on the electrode placement. Invasive BCIs use electrodes implanted directly into the cortex, semi-invasive (e.g., ECoG) place electrodes on the brain surface, and non-invasive BCIs typically use EEG electrodes placed on the scalp.
What are the common applications of BCI technology?
BCI technology is used in various applications, including assistive communication for paralyzed individuals, motor rehabilitation after stroke, control of prosthetic limbs, neurofeedback for mental health, and cognitive enhancement. It also has potential in gaming and virtual reality.
What are the major challenges in BCI research?
Major challenges include signal non-stationarity, user variability, low signal-to-noise ratio, long-term stability of implanted electrodes, and the need for user training. Additionally, ethical and societal concerns regarding privacy and autonomy are important considerations.
How does deep learning improve BCI performance?
Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can automatically extract relevant features from raw neural signals, reducing the need for manual feature engineering. They have been shown to improve classification accuracy and robustness in BCI systems.
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