A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.

Background: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.

Methods: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.

Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.

Objective: Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice.

Approach: In this pilot study, we made a first step toward scenarios in which no ground truth is available.

Self-Recording of Eye Movements in Amyotrophic Lateral Sclerosis Patients Using a Smartphone Eye-Tracking App.

Introduction: Amyotrophic lateral sclerosis (ALS) can affect various eye movements, making eye tracking a potential means for disease monitoring. In this study, we evaluated the feasibility of ALS patients self-recording their eye movements using the "EyePhone," a smartphone eye-tracking application.

Methods: We prospectively enrolled ten participants and provided them with an iPhone equipped with the EyePhone app and a PowerPoint presentation with step-by-step recording instructions.

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication.

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.