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2026-03-23 00:00 China Clinical Launch & Registration Translated from EN

China Trial Registry Lists BCI Study for Post-Stroke Functional Recovery

Summary A new entry in the Chinese Clinical Trial Registry covers a study using brain-computer interface technology for functional recovery after stroke. The record, ChiCTR2600120924, was posted on March 23, 2026, and lists its status as not yet recruiting. The registry listing carries only bibliographic details, so the trial design, sample size and sponsor remain undisclosed.
Why it matters The entry places a BCI directly in the post-stroke rehabilitation setting, and its registry number and posting date can be checked. What is confirmed so far is only the bibliographic record: status is not yet recruiting, and the protocol, sample size and sponsor are still undisclosed, so this is a starting point rather than a result. For readers tracking stroke recovery, the details that will matter are how the BCI reads signals, how it drives training, and when the primary endpoint is set.

BCIwiki (bciwiki.com) — The Chinese Clinical Trial Registry (ChiCTR) has posted a study on functional recovery after stroke based on brain-computer interface technology, under registration number ChiCTR2600120924, dated March 23, 2026. The record lists the trial's status as not yet recruiting. The entry is carried by the World Health Organization's International Clinical Trials Registry Platform (WHO ICTRP).

Only bibliographic details are public so far. The trial design, sample size, sponsor and primary endpoint are not disclosed in the registry summary, so it is not possible to tell whether the study uses an invasive or non-invasive approach, or to confirm the intervention and follow-up duration. The title links BCI technology directly to post-stroke functional recovery, indicating the study examines brain-signal reading and rehabilitation training within a single workflow.

A common approach to BCI in stroke rehabilitation asks patients to attempt motor imagery or movement while the system records and decodes signals such as EEG, then drives an exoskeleton, electrical stimulation or virtual-reality feedback to close the loop. Whether that path adds benefit beyond conventional rehabilitation depends on the control condition, training dose and endpoint selection, none of which are visible in this registration.

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