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

NCT02787200 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2016-06-08

No results posted yet for this study

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.

Conditions

  • EEG Data Analysis
  • Healthy Subjects

Sponsors & Collaborators

  • National Taiwan University Hospital

    lead OTHER

Principal Investigators

  • Tsung-Ren Huang · National Taiwan University

Eligibility

Min Age
20 Years
Max Age
40 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2016-05-31
Primary Completion
2017-05-31
Completion
2017-05-31

Countries

  • Taiwan

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT02787200 on ClinicalTrials.gov