Machine learning algorithm for electroencephalography (EEG) based brain signal analysis
Emotions are one of the many ways humans communicate with one another. One of the ways to record these emotions is by collecting brain signals via electroencephalography (EEG). There are many applications that this can be used for, be it in the medical field or for artificial intelligence purposes....
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/72041 |
| _version_ | 1824455603576111104 |
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| author | Teo, Jeffrey Eng Hock |
| author2 | Ser Wee |
| author_facet | Ser Wee Teo, Jeffrey Eng Hock |
| author_sort | Teo, Jeffrey Eng Hock |
| collection | NTU |
| description | Emotions are one of the many ways humans communicate with one another. One of the ways to record these emotions is by collecting brain signals via electroencephalography (EEG). There are many applications that this can be used for, be it in the medical field or for artificial intelligence purposes.
The main objective of this study is to develop a machine learning algorithm that can automatically predict the emotional state (Happy or Sad) of a human, based on the EEG signals.
This is done in 3 stages. The first stage involves the extraction of features in the alpha and beta band, from data that have been collected. The feature extracted is Discrete Wavelet Transform approximate coefficient at level 1. The next stage is to select the features using the Fisher’s ratio. The final stage is to use various classification methods to classify the data and test the accuracy of the models. The 3 classifiers being evaluated are Linear Discriminant Analysis (LDA), Linear Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). A 6-fold cross validation was used.
After evaluation of the 3 classification methods, it was found that LDA works best with an accuracy of 92.9% in the alpha band. For beta band, KNN gives the best prediction accuracy of 92.9%. When analysing both alpha and beta bands, KNN was found to be the best classifier to predict the emotional state (Happy or Sad). |
| first_indexed | 2025-02-19T03:40:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/72041 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:40:50Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/720412023-07-07T16:48:56Z Machine learning algorithm for electroencephalography (EEG) based brain signal analysis Teo, Jeffrey Eng Hock Ser Wee School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Emotions are one of the many ways humans communicate with one another. One of the ways to record these emotions is by collecting brain signals via electroencephalography (EEG). There are many applications that this can be used for, be it in the medical field or for artificial intelligence purposes. The main objective of this study is to develop a machine learning algorithm that can automatically predict the emotional state (Happy or Sad) of a human, based on the EEG signals. This is done in 3 stages. The first stage involves the extraction of features in the alpha and beta band, from data that have been collected. The feature extracted is Discrete Wavelet Transform approximate coefficient at level 1. The next stage is to select the features using the Fisher’s ratio. The final stage is to use various classification methods to classify the data and test the accuracy of the models. The 3 classifiers being evaluated are Linear Discriminant Analysis (LDA), Linear Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). A 6-fold cross validation was used. After evaluation of the 3 classification methods, it was found that LDA works best with an accuracy of 92.9% in the alpha band. For beta band, KNN gives the best prediction accuracy of 92.9%. When analysing both alpha and beta bands, KNN was found to be the best classifier to predict the emotional state (Happy or Sad). Bachelor of Engineering 2017-05-24T01:26:20Z 2017-05-24T01:26:20Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72041 en Nanyang Technological University 76 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Teo, Jeffrey Eng Hock Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title | Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title_full | Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title_fullStr | Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title_full_unstemmed | Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title_short | Machine learning algorithm for electroencephalography (EEG) based brain signal analysis |
| title_sort | machine learning algorithm for electroencephalography eeg based brain signal analysis |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/72041 |
| work_keys_str_mv | AT teojeffreyenghock machinelearningalgorithmforelectroencephalographyeegbasedbrainsignalanalysis |