Brain Training System Using Electroencephalography (EEG) for Neurorehabilitation of Hand Function After Stroke

Chinese University of Hong Kong45 enrolled

Overview

In this project we will design a control algorithm to identify the focalized motor-related clusters for hand function on each stroke patient. A real-time feedback will control the robotic hand when motor imagery is successfully identified. Finally, the focalized BCI-robot training will be compared in a randomized controlled trial to evaluate the training effectiveness and changes in the EEG patterns.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Enrollment

Conditions

Stroke

Interventions

Brain Computer InterfacePROCEDURE

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: (1) sufficient cognition to follow simple instructions, as well as understand the content and purpose of the experiment (Mini-Mental State Examination (MMSE) score\>25) ; (2) subcortical location of the ischemic lesion within the territory of the middle cerebral artery; (3) have moderate to severe motor disability at the paretic upper limb (assessed by Fugl-Meyer Assessment (FMA), Modified Ashworth Score of fingers (MAS), and Action Research Arm Test (ARAT)) Exclusion Criteria: (1) severe hand spasticity, open hand wound or hand deformity; (2) visual field deficits; (3) aphasia, neglect, and apraxia.

Locations (1)

Division of Biomedical Engineering, Dept Electronic Engineering

Hong Kong, Hong Kong

RECRUITING

Outcomes

Primary Outcomes

FMA-UE

Time frame: 3-month followup

Secondary Outcomes

ARAT

Time frame: 3-month followup

MAS

Time frame: 3-month followup

Data from ClinicalTrials.gov

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