Deep Analysis of BCI Neural Signals Based on AI Intent Prediction
Research Article
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Deep Analysis of BCI Neural Signals Based on AI Intent Prediction

Haoze Li 1*
1 School of Digital Science, Shanghai Lida University, Shanghai, China, 201620
*Corresponding author: 1492948238@qq.com
Published on 5 November 2025
Volume Cover
ACE Vol.203
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-515-8
ISBN (Online): 978-1-80590-516-5
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Abstract

In recent years, Brain-Computer Interface (BCI) technology has advanced rapidly, emerging as a critical bridge between the human brain and external devices with promising applications in medical rehabilitation, intelligent control, and other fields. However, the accurate parsing of neural signals and efficient prediction of user intent remain key challenges that hinder the widespread practical implementation of BCI systems. This study focuses on the deep analysis of BCI neural signals based on AI intent prediction, aiming to address two core research questions: how to enhance the accuracy of AI-based intent prediction through in-depth neural signal analysis, and which AI algorithms are most suitable for processing BCI neural signal data. First, relevant research progress was systematically summarized through a literature review. Then, the characteristics of BCI neural signals were analyzed using data analysis techniques. Finally, the effectiveness of different AI algorithms was verified via algorithm simulation. The research results demonstrate that integrating advanced AI algorithms with deep neural signal analysis technology can significantly improve the accuracy of intent prediction. This finding not only provides a new approach to overcoming the existing bottlenecks in BCI technology but also lays a theoretical and practical foundation for the further development and application of BCI systems.

Keywords:

AI Intent Prediction, Brain-Computer Interface (BCI), Neural Signal Analysis, BCI Technology

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Li,H. (2025). Deep Analysis of BCI Neural Signals Based on AI Intent Prediction. Applied and Computational Engineering,203,65-72.

References

[1]. Wolpaw, J. R., et al. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6): 767-791.

[2]. Nicolelis, M. A. (2001). Actions from thoughts. Nature, 409(6818): 403-407.

[3]. Schalk, G., et al. (2004). BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6): 1034-1043.

[4]. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30: 5998-6008.

[5]. Millán, J. d. R., et al. (2004). Adaptive brain-computer interfaces. Neural Networks, 17(10): 1593-1602.

[6]. Blankertz, B., et al. (2006). The Berlin brain-computer interface: progress beyond communication. Advances in Neural Information Processing Systems, 19: 135-142.

[7]. Pfurtscheller, G., Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7): 1123-1134.

[8]. Hochberg, L. R., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099): 164-171.

[9]. Donchin, E. (2000). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. IEEE Transactions on Rehabilitation Engineering, 8(2): 190-203.

[10]. Wolpaw, J. R., McFarland, D. J. (2004). Brain-computer interfaces: principles and practice. Oxford University Press.

[11]. Lotte, F., et al. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 4(2): R1-R13.

[12]. Blankertz, B., et al. (2008). Single-trial classification of EEG signals and its application to brain-computer interfaces. Journal of Neuroscience Methods, 167(1): 103-116.

[13]. LeCun, Y., et al. (2015). Deep learning. Nature, 521(7553): 436-444.

[14]. Schirrmeister, R. T., et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11): 5391-5420.

[15]. Wang, Y., et al. (2018). A review of deep learning based on brain-computer interface: recent progress and challenges. Frontiers in Neuroscience, 12: 55.

[16]. Goodfellow, I., et al. (2016). Deep Learning. MIT Press.

[17]. Haykin, S. (2009). Neural Networks and Learning Machines. Prentice Hall.

[18]. Devlin, J., et al. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv: 1810.04805.

References

[1]. Wolpaw, J. R., et al. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6): 767-791.

[2]. Nicolelis, M. A. (2001). Actions from thoughts. Nature, 409(6818): 403-407.

[3]. Schalk, G., et al. (2004). BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6): 1034-1043.

[4]. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30: 5998-6008.

[5]. Millán, J. d. R., et al. (2004). Adaptive brain-computer interfaces. Neural Networks, 17(10): 1593-1602.

[6]. Blankertz, B., et al. (2006). The Berlin brain-computer interface: progress beyond communication. Advances in Neural Information Processing Systems, 19: 135-142.

[7]. Pfurtscheller, G., Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7): 1123-1134.

[8]. Hochberg, L. R., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099): 164-171.

[9]. Donchin, E. (2000). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. IEEE Transactions on Rehabilitation Engineering, 8(2): 190-203.

[10]. Wolpaw, J. R., McFarland, D. J. (2004). Brain-computer interfaces: principles and practice. Oxford University Press.

[11]. Lotte, F., et al. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 4(2): R1-R13.

[12]. Blankertz, B., et al. (2008). Single-trial classification of EEG signals and its application to brain-computer interfaces. Journal of Neuroscience Methods, 167(1): 103-116.

[13]. LeCun, Y., et al. (2015). Deep learning. Nature, 521(7553): 436-444.

[14]. Schirrmeister, R. T., et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11): 5391-5420.

[15]. Wang, Y., et al. (2018). A review of deep learning based on brain-computer interface: recent progress and challenges. Frontiers in Neuroscience, 12: 55.

[16]. Goodfellow, I., et al. (2016). Deep Learning. MIT Press.

[17]. Haykin, S. (2009). Neural Networks and Learning Machines. Prentice Hall.

[18]. Devlin, J., et al. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv: 1810.04805.

Cite this article

Li,H. (2025). Deep Analysis of BCI Neural Signals Based on AI Intent Prediction. Applied and Computational Engineering,203,65-72.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-515-8(Print) / 978-1-80590-516-5(Online)
Editor: Marwan Omar, Guozheng Rao
Conference website: https://www.confspml.org/tianjin.html
Conference date: 21 December 2025
Series: Applied and Computational Engineering
Volume number: Vol.203
ISSN: 2755-2721(Print) / 2755-273X(Online)

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