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

• Conditions: Healthy Subjects; EEG Data Analysis

• Registration Number: NCT02787200
• Lead Sponsor: National Taiwan University Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2016-05
• Study First Submitted: 2016-05-26
• Study First Submitted QC: 2016-05-26
• Status Verified: 2016-06
• Study First Posted: 2016-06-01
• Last Update Submitted: 2016-06-06
• Last Update Posted: 2016-06-08
• Primary Completion: 2017-05
• Study Completion: 2017-05

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 50

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Time invariant components of EEG signals	One month

Trial Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan