View clinical trials related to Brainstem Stroke.
Filter by:The goal of this clinical trial is to demonstrate communication through a brain implant in people in locked-in state, i.e. people with severe paralysis and communication problems. The main questions it aims to answer are efficient and stable control of Brain-Computer interface (BCI) functions for communication with attempted hand movements and operation of a keyword-based speech BCI. Participants will be implanted with four electrode grids, with in total 128 electrodes, on the surface of the brain and a connector on the skull. Participation includes visits of researchers for recording and training at home, 2-3 times per week for one year. Extension of participation after one year is possible. If successful, the participant will be able to use the BCI at home independently, without the presence of a researcher.
This project adds to non-invasive BCIs for communication for adults with severe speech and physical impairments due to neurodegenerative diseases. Researchers will optimize & adapt BCI signal acquisition, signal processing, natural language processing, & clinical implementation. BCI-FIT relies on active inference and transfer learning to customize a completely adaptive intent estimation classifier to each user's multi-modality signals simultaneously. 3 specific aims are: 1. develop & evaluate methods for on-line & robust adaptation of multi-modal signal models to infer user intent; 2. develop & evaluate methods for efficient user intent inference through active querying, and 3. integrate partner & environment-supported language interaction & letter/word supplementation as input modality. The same 4 dependent variables are measured in each SA: typing speed, typing accuracy, information transfer rate (ITR), & user experience (UX) feedback. Four alternating-treatments single case experimental research designs will test hypotheses about optimizing user performance and technology performance for each aim.Tasks include copy-spelling with BCI-FIT to explore the effects of multi-modal access method configurations (SA1.3a), adaptive signal modeling (SA1.3b), & active querying (SA2.2), and story retell to examine the effects of language model enhancements. Five people with SSPI will be recruited for each study. Control participants will be recruited for experiments in SA2.2 and SA3.4. Study hypotheses are: (SA1.3a) A customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to the standard P300 matrix speller. (SA1.3b) Adaptive signal modeling will allow people with SSPI to typing accurately during a copy-spelling task with BCI-FIT without training a new model before each use. (SA2.2) Either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. (SA3.4) Language model enhancements, including a combination of partner and environmental input and word completion during typing, will improve typing performance with BCI-FIT, as measured by ITR during a story-retell task. Optimized recommendations for a multi-modal BCI for each end user will be established, based on an innovative combination of clinical expertise, user feedback, customized multi-modal sensor fusion, and reinforcement learning.
The CortiCom system consists of 510(k)-cleared components: platinum PMT subdural cortical electrode grids, a Blackrock Microsystems patient pedestal, and an external NeuroPort Neural Signal Processor. Up to two grids will be implanted in the brain, for a total channel count of up to 128 channels, for six months. In each participant, the grid(s) will be implanted over areas of cortex that encode speech and upper extremity movement.
The purpose of this research study is to demonstrate the safety and efficacy of using two CRS Arrays (microelectrodes) for long-term recording of brain motor cortex activity and microstimulation of brain sensory cortex.
The purpose of this research study is to demonstrate that individuals with upper limb paralysis due to spinal cord injury, brachial plexus injury, amyotrophic lateral sclerosis and brain stem stroke can successfully achieve direct brain control of assistive devices using an electrocorticography (ECoG)-based brain computer interface system.