Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals
Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial pat...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/5/2425 |
| _version_ | 1827752170434330624 |
|---|---|
| author | Vangelis P. Oikonomou |
| author_facet | Vangelis P. Oikonomou |
| author_sort | Vangelis P. Oikonomou |
| collection | DOAJ |
| description | Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain’s responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus. |
| first_indexed | 2024-03-11T07:12:03Z |
| format | Article |
| id | doaj.art-f15248b7757e44eb834b28acad3a2b1d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:12:03Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f15248b7757e44eb834b28acad3a2b1d2023-11-17T08:34:35ZengMDPI AGSensors1424-82202023-02-01235242510.3390/s23052425Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP SignalsVangelis P. Oikonomou0Information Technologies Institute, Centre for Research and Technology Hellas, Thermi-Thessaloniki, 57001 Thessaloniki, GreeceBrain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain’s responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.https://www.mdpi.com/1424-8220/23/5/2425brain biometricshuman recognitionsteady-state visual evoked potential signalsspatial filteringbrain–computer interfaces |
| spellingShingle | Vangelis P. Oikonomou Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals Sensors brain biometrics human recognition steady-state visual evoked potential signals spatial filtering brain–computer interfaces |
| title | Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals |
| title_full | Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals |
| title_fullStr | Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals |
| title_full_unstemmed | Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals |
| title_short | Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals |
| title_sort | human recognition using deep neural networks and spatial patterns of ssvep signals |
| topic | brain biometrics human recognition steady-state visual evoked potential signals spatial filtering brain–computer interfaces |
| url | https://www.mdpi.com/1424-8220/23/5/2425 |
| work_keys_str_mv | AT vangelispoikonomou humanrecognitionusingdeepneuralnetworksandspatialpatternsofssvepsignals |