Advances in brain computer interface for amyotrophic lateral sclerosis communication

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that often results in the loss of speech, creating significant communication barriers. Brain–computer interfaces (BCIs) provide a transformative solution for restoring communication and enhancing the quality of life for ALS individuals. Recent advances in implantable electrocorticographic systems have demonstrated the feasibility of synthesizing intelligible speech directly from neural activity. By recording high-resolution neural signals from motor, premotor, and somatosensory cortices with decoding algorithms, these systems can transform neural patterns into acoustic features and intelligible speech, providing natural and intuitive communication pathways for ALS individuals. Non-invasive electroencephalography, while lacking the spatial resolution of electrocorticographic systems, offers a safer alternative with high temporal resolution for capturing speech-related neural dynamics. When combined with robust feature extraction techniques, such as common spatial pattern and time-frequency analyses, as well as multimodal integration with functional near-infrared spectroscopy or electromyography, it effectively enhances decoding accuracy and system robustness. Despite the progress, challenges remain, including user variability, BCI illiteracy, and the impact of fatigue on system performance. Personalized models, adaptive algorithms, and secure frameworks for brain data privacy are essential for addressing these limitations, enabling BCIs to enhance accessibility and reliability. Advancing these technologies and methodologies holds immense promise for restoring independence and bridging the communication gap for individuals with ALS. Future research could focus on long-term clinical studies to evaluate the stability and effectiveness of these systems, as well as the development of more natural and unobtrusive BCI paradigms.