Hewlbern / WLClassification

Estimating the cognitive load in physical spatial 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]