Patent landscape of brain-machine interface technology

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.

Bioactive polymer-enabled conformal neural interface and its application strategies

Neural interface is a powerful tool to control the varying neuron activities in the brain, where the performance can directly affect the quality of recording neural signals and the reliability of in vivo connection between the brain and external equipment. Recent advances in bioelectronic innovation have provided promising pathways to fabricate flexible electrodes by integrating electrodes on bioactive polymer substrates. These bioactive polymer-based electrodes can enable the conformal contact with irregular tissue and result in low inflammation when compared to conventional rigid inorganic electrodes. In this review, we focus on the use of silk fibroin and cellulose biopolymers as well as certain synthetic polymers to offer the desired flexibility for constructing electrode substrates for a conformal neural interface. First, the development of a neural interface is reviewed, and the signal recording methods and tissue response features of the implanted electrodes are discussed in terms of biocompatibility and flexibility of corresponding neural interfaces. Following this, the material selection, structure design and integration of conformal neural interfaces accompanied by their effective applications are described. Finally, we offer our perspectives on the evolution of desired bioactive polymer-enabled neural interfaces, regarding the biocompatibility, electrical properties and mechanical softness.