Adaptive Multimodal EEG Signal Acquisition for Robust Real-World Brain–Computer Interfaces

Abstract: Brain–Computer Interfaces (BCIs) hold immense promise for restoring communication, enhancing motor function, and augmenting human capabilities. However, the widespread adoption and reliable performance of BCIs in real-world environments are significantly hampered by challenges inherent in neural signal acquisition. Traditional electroencephalography (EEG) systems, while non-invasive and portable, are highly susceptible to various forms of noise, including physiological artifacts (e.g., ocular, muscular, cardiac) and environmental interference. These contaminations severely degrade signal quality, compromise decoding accuracy, and limit the robustness of BCI applications outside controlled laboratory settings. This paper addresses the critical problem of neural signal degradation by proposing the Neuroba Adaptive Multimodal Signal Acquisition Architecture (NAMSAA). NAMSAA is a novel conceptual framework designed to dynamically integrate and fuse data from multiple physiological sensors, including EEG, electromyography (EMG), electrooculography (EOG), and optionally functional near-infrared spectroscopy (fNIRS). The architecture comprises five internal modules: Neural Signal Collection, Multimodal Sensor Fusion, Adaptive Signal Validation, Real-Time Noise Suppression, and Signal Standardization and Output. By leveraging adaptive signal processing techniques, context-aware acquisition, and quality-based prioritization, NAMSAA aims to significantly improve the signal-to-noise ratio, enhance artifact resilience, and standardize neural data for downstream processing. This framework is expected to contribute to the development of more robust, reliable, and user-adaptive BCIs, paving the way for their practical deployment in diverse real-world scenarios. While NAMSAA presents a comprehensive solution, its practical implementation faces limitations related to hardware constraints, computational demands, and ethical considerations. Future work will focus on AI-driven signal acquisition, neural foundation models, and edge computing to further advance self-adaptive acquisition systems and large-scale neural networks within the broader Neuroba NCTS Framework.