Advances in Brain-Computer Interface Technology: A Comprehensive Review of Neural Signal Processing and Applications
Authors: ZHANG Wei, LI Ming, WANG Fang, CHEN Jie, LIU Yang
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.
