# An EEG-based approach for Parkinson's disease diagnosis using Capsule network

arXiv: https://arxiv.org/abs/2201.00628 · HTML: https://mlsanity.com/paper/2201.00628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)

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

- [EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.13735/) (2025)
- [Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques](https://mlsanity.com/paper/2304.01214/) (2023)

## Abstract (arXiv)

As the second most common neurodegenerative disease, Parkinson's disease has caused serious problems worldwide. However, the cause and mechanism of PD are not clear, and no systematic early diagnosis and treatment of PD have been established. Many patients with PD have not been diagnosed or misdiagnosed. In this paper, we proposed an EEG-based approach to diagnosing Parkinson's disease. It mapped the frequency band energy of electroencephalogram(EEG) signals to 2-dimensional images using the interpolation method and identified classification using capsule network(CapsNet) and achieved 89.34% classification accuracy for short-term EEG sections. A comparison of separate classification accuracy across different EEG bands revealed the highest accuracy in the gamma bands, suggesting that we need to pay more attention to the changes in gamma band changes in the early stages of PD.