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

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT02787200

Enrollment

Registered

2016-06-01

Start date

2016-05-31

Completion date

2017-05-31

Last updated

2016-06-08

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

Conditions

EEG Data Analysis, Healthy Subjects

Keywords

electroencephalogram (EEG),Human brain,Brain-Computer Interface

Brief summary

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.

Interventions

None listed

Study design

Time perspective

CROSS_SECTIONAL

Eligibility

Sex/Gender

ALL

Age

20 Years to 40 Years

Healthy volunteers

Yes

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

Design outcomes

Primary

Measure	Time frame
Time invariant components of EEG signals	One month

Countries

Taiwan

Contacts

Primary ContactTsung-Ren Huang

trhuang@ntu.edu.tw886-23366-3104

Backup ContactMeng-Huan Wu

mhjasonwu@gmail.com886-3-3250462

Outcome results