Integration of neural networks in brain–computer interface applications: Research frontiers and trend analysis based on Python

Brain–computer interfaces (BCIs) establish a connection between the human brain and external devices, facilitating novel forms of communication and control. Although BCIs possess significant potential, there is a need for performance improvements. Integrating neural networks into BCIs is crucial for enhancing functionality and promoting broader adoption. This study examines 1,867 articles from the Web of Science core database, covering the period from 1996 to 2024, to identify contemporary hotspots and trends in the application of neural networks within BCIs. This study employs bibliometric methods and Python for analysis, examining collaborative relationships, citation networks, keyword bursts, and clustering with visual representations of the findings. The results indicated that current study hotspots predominantly center on “P300,” “Long Short-Term Memory,” “Motor Imagery,” “Epilepsy,” “Emotion Recognition,” “Feature Extraction,” and “Transfer Learning.” Future development directions encompass: (1) the establishment of public BCI datasets; (2) the exploration of diverse feature extraction and fusion methods; (3) the enhancement of machine learning and deep learning integration for improved performance and real-time processing; (4) the expansion of application scenarios and the development of portable devices; (5) the optimization of transfer learning algorithms to mitigate performance challenges arising from individual differences. This study provides an overview of the current research landscape and identifies potential future research directions. Furthermore, it assists practitioners in recognizing additional business opportunities and acts as a resource for the formulation of government policy.