Clinical trial NCT06861517: EEG-based Brain-computer Interface Database for Motor Rehabilitation
Official title: Deep Dictionaries for Feature Extraction in Context of Sparse Data for Electroencephalographic Signals from Brain-computer Interfaces
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Healthy Adults
Keywords: EEG Signals Database
BCI
Effective Motor Activity
Lead sponsor: Universidad Nacional de Entre Rios
Sponsor class: OTHER
First posted: 2025-03-06
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Volunteers of legal age and under 60 years of age, of any gender and without a history of neurological diseases

Brief summary: 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.


Eligibility criteria: Inclusion Criteria:

* Willingness and ability to fully understand the purpose and scope of the experiment and to comply with the experiment instructions.
* Ability to easily distinguish visually the figures in the study.
* Ability to perform tasks that demand sustained concentration.

Exclusion Criteria:

* History of neurological diseases.
* Suffering from any type of musculoskeletal disorder that limits the motor skills necessary for the experiment.
* Having a significant hearing loss that prevents him/her from hearing the study instructions.
* Pregnancy.
* Lack of cooperation.

Primary outcomes:
measure: Sensory motor rhythms power bands
timeFrame: Day 1
Secondary outcomes:
Other outcomes:
Locations:
city: Oro Verde
country: Argentina
facility: Faculty of Engineering, National University of Entre Ríos
state: Entre Ríos
Study officials:
References: