arXiv:2605.24313v1 Announce Type: new Abstract: Current high-performing intracortical speech neuroprostheses achieve low word error rates but typically rely on external language models during inference, increasing memory, computation, and latency. In this work, we investigate whether meaningful character-level decoding is achievable without such models. We propose an end-to-end Conformer-based neural decoder trained directly on intracortical recordings from a participant with amyotrophic lateral sclerosis (ALS). Without any external language model, the system achieves a character error rate (C
Advances in neural decoding and AI models are converging to enable direct brain-computer interfaces for communication, especially for those with severe motor impairments.
This breakthrough offers a potential new mode of communication for individuals with neurological conditions, reducing reliance on external AI models and offering greater autonomy.
This research demonstrates the viability of end-to-end, character-level speech decoding directly from intracortical activity without external language models, improving efficiency and potentially reducing latency.
- · Patients with ALS and similar conditions
- · Brain-computer interface developers
- · Neuroscience research institutions
- · Medical device companies
- · Companies manufacturing less efficient assistive communication devices
Improved quality of life and communication capabilities for individuals with severe motor impairments.
Accelerated development and adoption of advanced BCI technologies, expanding their applications beyond medical uses.
Ethical and societal debates surrounding brain privacy, digital identity, and the integration of neural technology into daily life.
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