Signal processing and machine learning for recognizing EEG signals of brain-computer interface
The human brain contains 86 billion nerve cells, the interaction activity of which makes human think and feel. Electroencephalography (EEG) is a physiological method to record brain-generated electrical activity through placing electrodes on the scalp surface. Brain-Computer interface, a device cons...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149797 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The human brain contains 86 billion nerve cells, the interaction activity of which makes human think and feel. Electroencephalography (EEG) is a physiological method to record brain-generated electrical activity through placing electrodes on the scalp surface. Brain-Computer interface, a device consists of electrodes, allow human to interact with computer by EEG measuring. Due to EEG signals high signal-to-noise ratio property, machine learning algorithm was applied for better features of interest extraction. This project aims to use machine learning approaches to achieve better EEG signal classification on human emotion with help of suitable feature extraction methods. |
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