# Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis

arXiv: https://arxiv.org/abs/2107.08514 · HTML: https://mlsanity.com/paper/2107.08514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [A review of feature extraction and performance evaluation in epileptic seizure detection using EEG](https://mlsanity.com/paper/1908.00492/) (2019)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare](https://mlsanity.com/paper/2001.09182/) (2020)
- [Deep learning denoising for EOG artifacts removal from EEG signals](https://mlsanity.com/paper/2009.08809/) (2020)
- [iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare](https://mlsanity.com/paper/1911.04471/) (2019)
- [A Robust and Accurate Deep Learning based Pattern Recognition Framework for Upper Limb Prosthesis using sEMG](https://mlsanity.com/paper/2106.02463/) (2021)

## Built on by (most cited first, 1 of 1)

- [Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare](https://mlsanity.com/paper/2111.11298/) (2021)

## Abstract (arXiv)

The Brain-Computer Interface system is a profoundly developing area of experimentation for Motor activities which plays vital role in decoding cognitive activities. Classification of Cognitive-Motor Imagery activities from EEG signals is a critical task. Hence proposed a unique algorithm for classifying left/right-hand movements by utilizing Multi-layer Perceptron Neural Network. Handcrafted statistical Time domain and Power spectral density frequency domain features were extracted and obtained a combined accuracy of 96.02%. Results were compared with the deep learning framework. In addition to accuracy, Precision, F1-Score, and recall was considered as the performance metrics. The intervention of unwanted signals contaminates the EEG signals which influence the performance of the algorithm. Therefore, a novel approach was approached to remove the artifacts using Independent Components Analysis which boosted the performance. Following the selection of appropriate feature vectors that provided acceptable accuracy. The same method was used on all nine subjects. As a result, intra-subject accuracy was obtained for 9 subjects 94.72%. The results show that the proposed approach would be useful to classify the upper limb movements accurately.