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NCT06861517

EEG-based Brain-computer Interface Database for Motor Rehabilitation

Completed Last updated 6 March 2025
What this trial tests

trial in Healthy Adults in 30 participants. Completed in 1 December 2024.

Timeline
2 September 2024
Primary endpoint
1 December 2024
1 December 2024

Quick facts

Lead sponsorJaime Alejandro Quiroga Forero
StatusCompleted
Study typeOBSERVATIONAL
Enrollment30
Start date2 September 2024
Primary completion1 December 2024
Estimated completion1 December 2024
Sites1 location across Argentina

Conditions studied

Sponsor

Jaime Alejandro Quiroga Forero

Who can join

Adults 18 to 60, any sex, with Healthy Adults. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The human brain, as a processing center, controls bodily, cognitive, emotional and social functions, enabling perception, signal analysis and decision making. However, these functions can be affected by acquired brain injury (ABI), resulting from traumatic (blows to the head) or non-traumatic factors (tumors, strokes, infections, among others). Annually, about 55 million new cases of ABI are reported, with sequelae that can affect the quality of life of patients and their families. This scenario has driven research into tools to mitigate and recover lost capabilities. The Center for Rehabilitation Engineering and Neuromuscular and Sensory Research (CIRINS) of the Faculty of Engineering of the National University of Entre Ríos in Argentina has developed neuromuscular and sensory rehabilitation systems, with a focus on the innovation of motor rehabilitation tools using EEG-based brain-computer interfaces (BCI). These BCIs stand out for their economy and versatility, showing significant effects in the rehabilitation of motor functions. Challenges in BCI include signal complexity, artifacts, and inter-person variability, making it difficult to estimate user intent and extending calibration time. To mitigate these problems, strategies based on Deep Learning and dictionary learning have been proposed, which allow for sparse representations of data, being robust to noise and missing data, but with challenges in classification. The study proposes to develop a database of electroencephalographic signals applicable in the development of new algorithms for processing and feature extraction of this type of signals, contributing to the development of technology that supports rehabilitation processes.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.

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