About this Research Topic
Speech brain-computer interfaces (BCIs) have shown great potential in restoring rapid communication for people with paralysis by decoding neural activity related to attempted speech into text or sound. Recent advancements in this field have demonstrated promising results, such as achieving a 9.1% word error rate on a 50-word vocabulary and a 23.8% word error rate on a 125,000-word vocabulary. However, current BCIs have not yet reached the accuracy levels required for unconstrained communication from a large vocabulary. Additionally, there is a need for further investigation into the neural code for speech and the development of more effective decoding algorithms for real-time applications.
This Research Topic aims to advance the field of speech BCIs by improving the accuracy and speed of communication for individuals with paralysis. The main objectives include exploring novel decoding strategies, optimizing machine learning models for speech reconstruction, and investigating the neural code for speech to better understand its underlying mechanisms. By addressing these challenges, the goal is to develop more effective and efficient speech BCIs that can restore rapid communication for people who can no longer speak due to paralysis.
To gather further insights in the development and optimization of speech BCIs, we welcome articles addressing, but not limited to, the following themes:
- Novel decoding strategies for speech BCIs
- Optimization of machine learning models for speech reconstruction
- Investigation of the neural code for speech and its implications for speech BCIs
- Comparison of different neural network architectures for real-time decoding of speech-related neural signals
- Development of closed-loop synthesis of artificial speech sounds from human cortical surface recordings
- Integration of brain-computer interfaces with functional electrical stimulation (FES) for enhanced communication in locked-in individuals
- Evaluation of BCI-ES interventions for upper limb movement rehabilitation in stroke and spinal cord injury populations
- Standardization and clinical implementation of BCI-ES systems for improved communication and rehabilitation outcomes.
This Research Topic aims to advance the field of speech BCIs by improving the accuracy and speed of communication for individuals with paralysis. The main objectives include exploring novel decoding strategies, optimizing machine learning models for speech reconstruction, and investigating the neural code for speech to better understand its underlying mechanisms. By addressing these challenges, the goal is to develop more effective and efficient speech BCIs that can restore rapid communication for people who can no longer speak due to paralysis.
To gather further insights in the development and optimization of speech BCIs, we welcome articles addressing, but not limited to, the following themes:
- Novel decoding strategies for speech BCIs
- Optimization of machine learning models for speech reconstruction
- Investigation of the neural code for speech and its implications for speech BCIs
- Comparison of different neural network architectures for real-time decoding of speech-related neural signals
- Development of closed-loop synthesis of artificial speech sounds from human cortical surface recordings
- Integration of brain-computer interfaces with functional electrical stimulation (FES) for enhanced communication in locked-in individuals
- Evaluation of BCI-ES interventions for upper limb movement rehabilitation in stroke and spinal cord injury populations
- Standardization and clinical implementation of BCI-ES systems for improved communication and rehabilitation outcomes.
Keywords: brain-computer interfaces, speech, paralysis, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
