Identification of Time-invariant EEG Signals for Brain-Computer Interface

National Taiwan University Hospital50 enrolled

Overview

This study aims to identify various time-variant and time-invariant components of EEG signals using advanced signal processing techniques, such as machine learning. The investigators' ultimate goal is to develop universal or customised brain-computer interface that are stable across days or even years.

Study Type

OBSERVATIONAL

Enrollment

Conditions

EEG Data Analysis Healthy Subjects

Eligibility

Sex: ALLMin age: 20 YearsMax age: 40 YearsHealthy volunteers:

Medical Language ↔ Plain English

Exclusion criteria: 1. Severe vision disorders which prevent volunteers to recognize instructions on the screen 2. Severe psychiatric disorders 3. Severe sleep disorders which keep volunteers awake for two hours 4. Volunteers with claustrophobia 5. Patients who underwent stroke and brain surgery 6. Patients with neuromuscular diseases

Locations (1)

National Taiwan University Hospital

Taipei, Taipei, Taiwan

RECRUITING

Outcomes

Primary Outcomes

Time invariant components of EEG signals

Time frame: One month

Central Contacts

Tsung-Ren Huang

CONTACT

886-23366-3104 trhuang@ntu.edu.tw

Meng-Huan Wu

CONTACT

886-3-3250462 mhjasonwu@gmail.com

Data from ClinicalTrials.gov

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