From Limitations to Future: ML/AI in Advancing BCI
DOI:
https://doi.org/10.54097/qmrpcv61Keywords:
Brain-Computer Interface; EEG; Machine Learning; Artificial Intelligence; Signal Processing; Deep Learning.Abstract
Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices and has broad applications in rehabilitation, assistive communication, and human-computer interaction. However, traditional EEG-based BCI methods, including spatial filtering and time-frequency analysis, are limited by low signal-to-noise ratio, non-stationary signals, artifact interference, manual feature extraction, and weak adaptability across individuals. This paper analyzes these limitations and discusses how machine learning and artificial intelligence can improve BCI systems. Convolutional neural networks can automatically extract spatial-temporal EEG features, attention mechanisms can strengthen task-relevant channels and frequency bands, lightweight convolutional models can support real-time decoding, and transfer learning with data augmentation can improve individual adaptation. These methods help enhance the accuracy, robustness, and practicality of BCI systems. With further development, ML/AI-based BCI technologies are expected to play an increasingly important role in medical rehabilitation, daily assistance, and intelligent human-computer interaction.
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