| Title |
Feature extraction and classification for motor imagery in EEG signals / |
| Translation of Title |
Numanomų motorikos požymių išskyrimas ir klasifikavimas elektroencefalografiniuose signaluose. |
| Authors |
Prasad, Aravind |
| Full Text |
|
| Pages |
57 |
| Keywords [eng] |
EEG ; BCIi ; Hjorth ; LDA ; motor imagery |
| Abstract [eng] |
Electroencephalography is a non-invasive technique which is used for recording the neurophysiological reactions in the brain. It measures the activity of neurons. This report consists of different steps taken for finding that it is possible to control bionic arm with imaginary data of motor movement. The electroencephalographic signals were obtained from Physionet biosignal database. Feature extraction and its analysis is done for ten subjects. The different features were calculated for different segments of the obtained signal. The features extracted were inspired by Hjorth parameters and a higher order statistics - kurtosis. The signal processing algorithm for the process is explained in the report. The supervised feature classification is implemented using the Linear Discriminant Analysis. The obtained accuracy for the classifier was found to be around 60-70% depending on the electrodes and type of data (real or imaginary). |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2016 |
