Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications
The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, t...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/390873 |
| _version_ | 1797206785484914688 |
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| author | Stephan Stephe Thangaiyan Jayasankar* Kalimuthu Vinoth Kumar |
| author_facet | Stephan Stephe Thangaiyan Jayasankar* Kalimuthu Vinoth Kumar |
| author_sort | Stephan Stephe |
| collection | DOAJ |
| description | The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex. |
| first_indexed | 2024-04-24T09:12:32Z |
| format | Article |
| id | doaj.art-4e3b2162682646ce955a4ffdccf82e56 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:12:32Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-4e3b2162682646ce955a4ffdccf82e562024-04-15T17:26:35ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-012919210010.17559/TV-20210121112228Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI ApplicationsStephan Stephe0Thangaiyan Jayasankar*1Kalimuthu Vinoth Kumar2Department of ECE, University College of Engineering (BIT Campus), Tiruchirappalli, Tamilnadu, IndiaDepartment of ECE, University College of Engineering (BIT Campus), Tiruchirappalli, Tamilnadu, IndiaDepartment of ECE, SSM Institute of Engineering and Technology, Dindigul, Tamilnadu, IndiaThe activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex.https://hrcak.srce.hr/file/390873convolutional neural network (CNN)electroencephalogram (EEG)empirical mode decomposition (EMD)generative adversarial network (GAN)intrinsic mode function (IMF)motor imagery (MI) |
| spellingShingle | Stephan Stephe Thangaiyan Jayasankar* Kalimuthu Vinoth Kumar Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications Tehnički Vjesnik convolutional neural network (CNN) electroencephalogram (EEG) empirical mode decomposition (EMD) generative adversarial network (GAN) intrinsic mode function (IMF) motor imagery (MI) |
| title | Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications |
| title_full | Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications |
| title_fullStr | Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications |
| title_full_unstemmed | Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications |
| title_short | Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications |
| title_sort | motor imagery eeg recognition using deep generative adversarial network with emd for bci applications |
| topic | convolutional neural network (CNN) electroencephalogram (EEG) empirical mode decomposition (EMD) generative adversarial network (GAN) intrinsic mode function (IMF) motor imagery (MI) |
| url | https://hrcak.srce.hr/file/390873 |
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