User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features
In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Internacional de La Rioja (UNIR)
2019-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/node/2806 |
| _version_ | 1818411397955977216 |
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| author | Loay George Hend Hadi |
| author_facet | Loay George Hend Hadi |
| author_sort | Loay George |
| collection | DOAJ |
| description | In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset. |
| first_indexed | 2024-12-14T10:30:46Z |
| format | Article |
| id | doaj.art-1b1bbc8dabc24b42bd9f4b26cf4e0a4e |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-14T10:30:46Z |
| publishDate | 2019-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-1b1bbc8dabc24b42bd9f4b26cf4e0a4e2022-12-21T23:06:08ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602019-06-0155546210.9781/ijimai.2018.12.008ijimai.2018.12.008User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based FeaturesLoay GeorgeHend HadiIn this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset.http://www.ijimai.org/journal/node/2806DCTDiscrete Fourier TransformDiscrete Wavelet TransformsElectroencephalographyEnergyEuclidean DistanceStatistical Moments |
| spellingShingle | Loay George Hend Hadi User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features International Journal of Interactive Multimedia and Artificial Intelligence DCT Discrete Fourier Transform Discrete Wavelet Transforms Electroencephalography Energy Euclidean Distance Statistical Moments |
| title | User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features |
| title_full | User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features |
| title_fullStr | User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features |
| title_full_unstemmed | User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features |
| title_short | User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features |
| title_sort | user identification and verification from a pair of simultaneous eeg channels using transform based features |
| topic | DCT Discrete Fourier Transform Discrete Wavelet Transforms Electroencephalography Energy Euclidean Distance Statistical Moments |
| url | http://www.ijimai.org/journal/node/2806 |
| work_keys_str_mv | AT loaygeorge useridentificationandverificationfromapairofsimultaneouseegchannelsusingtransformbasedfeatures AT hendhadi useridentificationandverificationfromapairofsimultaneouseegchannelsusingtransformbasedfeatures |