Similarity Analysis of EEG Data Based on Self Organizing Map Neural Network

Advances in Electrical and Electronic Engineering

Vaclav Snasel¹  Michal Prilepok¹  Ibrahim Salem Jahan²  Marek Penhaker³ 

[1] Department of Computer Science, Faculty of Electrical Engineering and Computer Science, IT4 Innovations, European Center of Excellence VSB - Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic;Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic;Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic;

关键词: eeg data;    electroencephalograph;    polynomial curve fitting;    som;    unsupervised learning.;   

DOI  :  10.15598/aeee.v12i5.1171

来源: DOAJ

【 摘 要 】

The Electroencephalography (EEG) is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI) by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM) as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5~Hz to 60~Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly.

【 授权许可】

Unknown