Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4...
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| Format: | Book Section |
| Language: | English |
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Institute of Graduate Studies, UiTM
2017
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| Online Access: | https://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf |
| _version_ | 1825735303112949760 |
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| author | Fuad, Norfaiza |
| author_facet | Fuad, Norfaiza |
| author_sort | Fuad, Norfaiza |
| collection | UITM |
| description | The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced). There are four (4) sub-bands of frequency for EEG signals; δ band (0.2- 3 Hz), θ band (3- 8 Hz), α band (8-12 Hz) and β band (12-30 Hz). These sub-bands can be used to analyze human brain activities. This research involved 96 healthy subjects for EEG data collection. The EEG 3D signals are produced through signal processing and image processing techniques. The development of 3D involved preprocessing of raw EEG signals and construction of 2D EEG images or spectrograms. EEG signals are pre-processed using artifact removal and band pass filter technique. The resultant images for 2D EEG image are constructed via Short Time Fourier Transform (STFT). Power spectral density (PSD) values are extracted as features. Some techniques for data analysis like Shapiro-Wilk for data distribution analysis and Pearson correlation for data correlation analysis have been implemented. These features are analyzed to signify the pattern for brain balanced index. |
| first_indexed | 2024-03-06T01:45:02Z |
| format | Book Section |
| id | oai:ir.uitm.edu.my:19922 |
| institution | Universiti Teknologi MARA |
| language | English |
| last_indexed | 2024-03-06T01:45:02Z |
| publishDate | 2017 |
| publisher | Institute of Graduate Studies, UiTM |
| record_format | dspace |
| spelling | oai:ir.uitm.edu.my:199222018-06-11T02:30:22Z https://ir.uitm.edu.my/id/eprint/19922/ Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad Fuad, Norfaiza Malaysia The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced). There are four (4) sub-bands of frequency for EEG signals; δ band (0.2- 3 Hz), θ band (3- 8 Hz), α band (8-12 Hz) and β band (12-30 Hz). These sub-bands can be used to analyze human brain activities. This research involved 96 healthy subjects for EEG data collection. The EEG 3D signals are produced through signal processing and image processing techniques. The development of 3D involved preprocessing of raw EEG signals and construction of 2D EEG images or spectrograms. EEG signals are pre-processed using artifact removal and band pass filter technique. The resultant images for 2D EEG image are constructed via Short Time Fourier Transform (STFT). Power spectral density (PSD) values are extracted as features. Some techniques for data analysis like Shapiro-Wilk for data distribution analysis and Pearson correlation for data correlation analysis have been implemented. These features are analyzed to signify the pattern for brain balanced index. Institute of Graduate Studies, UiTM 2017 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad. (2017) In: The Doctoral Research Abstracts. IGS Biannual Publication, 12 (12). Institute of Graduate Studies, UiTM, Shah Alam. |
| spellingShingle | Malaysia Fuad, Norfaiza Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title | Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title_full | Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title_fullStr | Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title_full_unstemmed | Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title_short | Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad |
| title_sort | development of a 3d eeg feature extraction for brain balanced index bbi using artificial neural network ann norfaiza fuad |
| topic | Malaysia |
| url | https://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf |
| work_keys_str_mv | AT fuadnorfaiza developmentofa3deegfeatureextractionforbrainbalancedindexbbiusingartificialneuralnetworkannnorfaizafuad |