| Title |
Multi-class EEG signal classification and acquisition system for brain-computer interface / |
| Translation of Title |
Daugiaklasio EEG signalo klasifikavimo ir įrašymo sistema smegenų-kompiuterio sąsajai. |
| Authors |
Uktveris, Tomas |
| Full Text |
|
| Pages |
102 |
| Keywords [eng] |
brain-computer interface ; convolutional neural networks ; classification ; feature extraction ; EEG system |
| Abstract [eng] |
The dissertation analyzes brain-computer interface (BCI) four-class motor imagery (MI) classification problem and the development of tools for the brain electroencephalogram (EEG) acquisition. Multiple feature extraction and classification methods have been investigated and tested using computational software and experimental analysis methods. A new feature extraction channel difference method has been proposed for the EEG data processing based on Bandpower and Laplace filtering approaches. The proposed algorithm gives a similar filtering performance to a well-known CSP (common spatial patterns) algorithm. Also, a new method for a single dimension (1D) feature vector adaptation to two-dimensional (2D) feature maps has been proposed. The algorithm has been successfully validated during the experiments. The convolutional neural networks (CNN) based classification method has been adapted to solve four-class MI problem, and the experimentally acquired results were close to the other state-of-the-art methods. Moreover, a stackable and modular EEG acquisition hardware system for MI has been developed to help record second four-class validation EEG dataset and spread BCI among the wider audience. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Doctoral thesis |
| Language |
English |
| Publication date |
2019 |
