Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator
| Autor: | Ipek Midi, Vedat Topuz, Ayça Ak |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Interface (computing) Deep learning Biomedical Engineering Health Informatics Image processing Electroencephalography Motor imagery Signal Processing medicine Spectrogram Computer vision Artificial intelligence business Robotic arm Brain–computer interface |
| Zdroj: | Biomedical Signal Processing and Control. 72:103295 |
| ISSN: | 1746-8094 |
| Popis: | Controlling of a robotic arm using a brain-computer interface (BCI) is one of the most impressive applications. In this study, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for BCI. EEG signals are divided into three secondary tables, which were converted into spectrogram images. After applying the spectrogram method, the obtained images are divided into folder structures and deep learning is performed. In the deep learning stage, 400 images are obtained for each task as input to the GoogLeNet. After the deep learning completed, the presented system has been tested to imagine up, down, left and right movement to control the movement of the robot arm. It is observed that the robot arm performs the desired movement over 90% accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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