satriabw/humandataproject

Repository for the Human Data Analytics course of UniPD.

Human Data Analytics Project

Welcome to the GitHub repository for our final project in the Human Data Analytics course at the University of Padua (UniPD)! This project focuses on analyzing EEG data through advanced neural network architectures, particularly exploring various configurations of Recurrent Neural Networks (RNNs) within a Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) framework.

To evaluate the models results from W&B, please visit:

https://wandb.ai/bdma/hda-big-3

The full report can be reviewed in:

Report PDF

Overview

The objective of this study is to scrutinize the electromagnetic responses elicited by different cerebral regions when exposed to specific stimuli. We aim to develop efficient algorithms capable of handling the high dimensionality and computational demands typical of EEG data processing.

Key Features

• Implementation of a CNN-RNN framework for EEG signal analysis.
• Comparative analysis of different RNN layers, including Vanilla RNNs, GRUs, LSTMs, and CfC networks, alongside innovative NCP wiring.
• Focus on the Hand Leg Tongue (HaLT) paradigm, analyzing responses to images of hands, feet, and tongues.
• Comprehensive data preprocessing strategies, including data augmentation techniques like smoothing and downsampling.

Getting Started

To dive into our project, clone this repository using:

git clone https://github.com/your-username/HumanDataProject.git

Run the Demo

To run the demo, please run:

python hda/demo.py --model {model_name} --version {version}

with the model_name and version from the wandb experiments

License

This project is open source and available under the MIT License.

Acknowledgements

• Gratitude to the University of Padua and the instructors of the Human Data Analytics course for their guidance and support.

Thank you for visiting our project repository! We hope you find this work insightful and useful for your own research or projects.

References

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[3] M. Lechner, R. Hasani, A. Amini, T. A. Henzinger, D. Rus, and R. Grosu, “Neural circuit policies enabling auditable autonomy,” Nature Machine Intelligence, vol. 2, pp. 642–652, Oct 2020.
[4] M. Kaya, M. K. Binli, E. Ozbay, H. Yanar, and Y. Mishchenko, “A large electroencephalographic motor imagery dataset for electroen- cephalographic brain computer interfaces,” Scientific Data, vol. 5, no. 1, p. 180211, 2018.
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[7] V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, “Eegnet: a compact convolutional neural network for eeg-based brainˆa C“computer interfaces,” Journal of Neural Engineering, vol. 15, p. 056013, jul 2018.
[8] W. Chen and K. Shi, “Multi-scale attention convolutional neural network for time series classification,” Neural Networks, vol. 136, pp. 126–140, 2021.
[9] D. Walther, J. Viehweg, J. Haueisen, and P. M ̃A¤der, “A systematic comparison of deep learning methods for eeg time series analysis,” Frontiers in Neuroinformatics, vol. 17, 2023.
[10] R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time- constant networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7657–7666, May 2021.
[11] A. Apicella, F. Isgr`o, A. Pollastro, and R. Prevete, “On the effects of data normalisation for domain adaptation on eeg data,” Engineering Applications of Artificial Intelligence, 2023.