Classification of EEG Spectrogram Using ANN for IQ Application
The intelligence term can be view in many areas such as linguistic, mathematical, music and art. In this paper, the Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The spectrogram images were formed from EEG signals, then th...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/3794/ http://umpir.ump.edu.my/id/eprint/3794/2/fkee_mahfuzah_2013.pdf |
| _version_ | 1848817299490865152 |
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| author | Mahfuzah, Mustafa Norizam, Sulaiman |
| author_facet | Mahfuzah, Mustafa Norizam, Sulaiman |
| author_sort | Mahfuzah, Mustafa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The intelligence term can be view in many areas
such as linguistic, mathematical, music and art. In this paper, the Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The spectrogram images were formed from EEG signals, then the Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from the images. This texture feature produced big matrix data, thus Principal Component Analysis (PCA) is used to reduce the big matrix. Then, ANN algorithm is employed to classify the EEG spectrogram image in IQ application. The
results will be validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN was able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE |
| first_indexed | 2025-11-15T01:19:34Z |
| format | Conference or Workshop Item |
| id | ump-3794 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:19:34Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-37942017-10-25T08:03:10Z http://umpir.ump.edu.my/id/eprint/3794/ Classification of EEG Spectrogram Using ANN for IQ Application Mahfuzah, Mustafa Norizam, Sulaiman TK Electrical engineering. Electronics Nuclear engineering The intelligence term can be view in many areas such as linguistic, mathematical, music and art. In this paper, the Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The spectrogram images were formed from EEG signals, then the Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from the images. This texture feature produced big matrix data, thus Principal Component Analysis (PCA) is used to reduce the big matrix. Then, ANN algorithm is employed to classify the EEG spectrogram image in IQ application. The results will be validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN was able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE 2013 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3794/2/fkee_mahfuzah_2013.pdf Mahfuzah, Mustafa and Norizam, Sulaiman (2013) Classification of EEG Spectrogram Using ANN for IQ Application. In: The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering , 9-11 Mei 2013 , Mevlana University, Turki. . (Unpublished) (Unpublished) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mahfuzah, Mustafa Norizam, Sulaiman Classification of EEG Spectrogram Using ANN for IQ Application |
| title | Classification of EEG Spectrogram Using ANN for IQ Application |
| title_full | Classification of EEG Spectrogram Using ANN for IQ Application |
| title_fullStr | Classification of EEG Spectrogram Using ANN for IQ Application |
| title_full_unstemmed | Classification of EEG Spectrogram Using ANN for IQ Application |
| title_short | Classification of EEG Spectrogram Using ANN for IQ Application |
| title_sort | classification of eeg spectrogram using ann for iq application |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/3794/ http://umpir.ump.edu.my/id/eprint/3794/2/fkee_mahfuzah_2013.pdf |