# Mental Task Classification Using Electroencephalogram Signal

arXiv: https://arxiv.org/abs/1910.03023 · HTML: https://mlsanity.com/paper/1910.03023/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN](https://mlsanity.com/paper/2303.11413/) (2023)
- [Deep Learning-Based Classification of Cognitive and Resting States Using Electroencephalography Signals](https://mlsanity.com/paper/2609.20467/) (2026)

## Abstract (arXiv)

This paper studies the classification problem on electroencephalogram (EEG) data of mental tasks, using standard architecture of three-layer CNN, stacked LSTM, stacked GRU. We further propose a novel classifier - a mixed LSTM model with a CNN decoder. A hyperparameter optimization on CNN shows validation accuracy of 72% and testing accuracy of 62%. The stacked LSTM and GRU models with FFT preprocessing and downsampling on data achieve 55% and 51% testing accuracy respectively. As for the mixed LSTM model with CNN decoder, validation accuracy of 75% and testing accuracy of 70% are obtained. We believe the mixed model is more robust and accurate than both CNN and LSTM individually, by using the CNN layer as a decoder for following LSTM layers. The code is completed in the framework of Pytorch and Keras. Results and code can be found at https://github.com/theyou21/BigProject.