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Robotic Exoskeleton Gait Training With and Without Brain-Computer Interface Control After Stroke

Comparison of the Therapeutic Effects Between Traditional and Brain-Computer Interface-Controlled Exoskeleton Gait Training in Post-Stroke Patients: A Randomized Comparative Study

Status
Completed
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07760116
Enrollment
42
Registered
2026-08-12
Start date
2025-01-01
Completion date
2026-07-30
Last updated
2026-08-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gait Disorders, Neurologic, Hemiparesis, Stroke

Brief summary

After a stroke, many people have trouble walking. One way to practice walking again is with a wearable robot, called an exoskeleton, that supports the legs and guides them through a walking pattern. In most systems, a therapist or a sensor decides when each step starts. This study looked at whether it helps to let a person's own brain activity start each step instead. Sensors placed on the scalp recorded brain signals. A computer read these signals and told the robot when to take a step. This way of controlling a device is called a brain-computer interface. People who had a stroke within the past year took part. They were placed by chance into one of two groups. One group practiced walking with the robot in the usual way. The other group practiced with the robot controlled by their own brain signals. Both groups trained twice a week for 4 weeks. Before and after the training, the study team measured how far each person could walk in 6 minutes, how long it took to stand up and walk a short distance and sit down again, how strong the knee muscles were, and how each person rated their own health and well-being. The study compared how much the two groups improved.

Interventions

DEVICEConventional exoskeleton gait training

Powered lower-limb exoskeleton gait training, twice weekly for 4 weeks. Each session included sit-to-stand, walking, and stand-to-sit phases. Phase transitions were triggered externally by a trained study assistant. No EEG decoding was used.

DEVICEBCI-controlled exoskeleton gait training

Same exoskeleton training schedule and phases. Each session began with a 6-minute EEG decoder calibration (theta, movement-related cortical potential, alpha). Each phase transition was triggered in a closed loop by the participant's decoded intent.

Sponsors

Taichung Veterans General Hospital
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
30 Years to 80 Years
Healthy volunteers
No

Inclusion criteria

* Age 30 to 80 years * Unilateral lower-limb weakness attributable to a stroke sustained within the previous year * Able to complete the Timed Up and Go (TUG) test at baseline * Able to follow training instructions * Provided written informed consent

Exclusion criteria

* Baseline Timed Up and Go (TUG) time of less than 20 seconds * Significant pain in the lower limbs * Prior lower-limb surgery * Neuromuscular disease affecting gait * Cardiopulmonary condition contraindicating gait training * Cognitive or cooperative deficits preventing the participant from following training instructions * Inability to perform the Timed Up and Go (TUG) test at baseline

Design outcomes

Primary

MeasureTime frameDescription
Change in 6-Minute Walk Test (6MWT) distanceBaseline and 4 weeks (immediately before and after the 4-week training program)Distance walked in 6 minutes along a flat 30-meter indoor corridor, recorded in meters. Participants were instructed to cover as much distance as possible. The same corridor was used for all assessments at both time points. Greater distance indicates better walking capacity.

Secondary

MeasureTime frameDescription
Change in SF-12 Physical Component Summary (PCS) scoreBaseline and 4 weeks (immediately before and after the 4-week training program)Physical component summary score of the 12-item Short Form Health Survey. Scores are norm-based to a population mean of 50 with a standard deviation of 10. Higher scores indicate better physical health-related quality of life.

Countries

Taiwan

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Aug 13, 2026