Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Estimating the cognitive load in physical spatial navigation

License

Notifications You must be signed in to change notification settings

thinknew/WLClassification

Folders and files

NameName
Last commit message
Last commit date

Latest commit

History

4 Commits

Repository files navigation

Estimating the cognitive load in physical spatial navigation

T. -T. N. Do, A. K. Singh, C. A. T. Cortes and C. -T. Lin, "Estimating the cognitive load in physical spatial navigation," 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 568-575, doi: 10.1109/SSCI47803.2020.9308389.

Requirements

  • Python == 3.7 or 3.8
  • tensorflow == 2.X (both for CPU and GPU)
  • PyRiemann >= 0.2.5
  • scikit-learn >= 0.20.1
  • matplotlib >= 2.2.3

How to run

  • Input Data Format: Number of EEG Channels x Number of Samples X Number of Trials for EEG data and Labels as vector. See testData.mat for references with sampling rate of 400 Hz.
  • Provide input data related information in 'op.py' such as path, sampling rate, number of classes, etc.
  • Execute the following line of code
python main.py

Models implemented/used

  • DeepConvNet [2]

DeepConvNet is based on repo [2]

About

Estimating the cognitive load in physical spatial navigation

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

AltStyle によって変換されたページ (->オリジナル) /