Decoding of imagined speech electroencephalography neural signals using transfer learning method
The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated me...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Journal of Physics Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2399-6528/ad0197 |
| _version_ | 1797654531550478336 |
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| author | Nrushingh Charan Mahapatra Prachet Bhuyan |
| author_facet | Nrushingh Charan Mahapatra Prachet Bhuyan |
| author_sort | Nrushingh Charan Mahapatra |
| collection | DOAJ |
| description | The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts. |
| first_indexed | 2024-03-11T17:00:50Z |
| format | Article |
| id | doaj.art-eb39bd6ac1e641fab35d8078de887f98 |
| institution | Directory Open Access Journal |
| issn | 2399-6528 |
| language | English |
| last_indexed | 2024-03-11T17:00:50Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics Communications |
| spelling | doaj.art-eb39bd6ac1e641fab35d8078de887f982023-10-20T11:17:59ZengIOP PublishingJournal of Physics Communications2399-65282023-01-017909500210.1088/2399-6528/ad0197Decoding of imagined speech electroencephalography neural signals using transfer learning methodNrushingh Charan Mahapatra0https://orcid.org/0000-0001-9166-5451Prachet Bhuyan1Intel Technology India Pvt Ltd , Bengaluru 560103, India; School of Computer Engineering, Kalinga Institute of Industrial Technology , Bhubaneswar 751024, IndiaSchool of Computer Engineering, Kalinga Institute of Industrial Technology , Bhubaneswar 751024, IndiaThe use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts.https://doi.org/10.1088/2399-6528/ad0197brain-computer interface (BCI)deep learning (DL)electroencephalography (EEG)imagined speechsignal processingtransfer learning (TL) |
| spellingShingle | Nrushingh Charan Mahapatra Prachet Bhuyan Decoding of imagined speech electroencephalography neural signals using transfer learning method Journal of Physics Communications brain-computer interface (BCI) deep learning (DL) electroencephalography (EEG) imagined speech signal processing transfer learning (TL) |
| title | Decoding of imagined speech electroencephalography neural signals using transfer learning method |
| title_full | Decoding of imagined speech electroencephalography neural signals using transfer learning method |
| title_fullStr | Decoding of imagined speech electroencephalography neural signals using transfer learning method |
| title_full_unstemmed | Decoding of imagined speech electroencephalography neural signals using transfer learning method |
| title_short | Decoding of imagined speech electroencephalography neural signals using transfer learning method |
| title_sort | decoding of imagined speech electroencephalography neural signals using transfer learning method |
| topic | brain-computer interface (BCI) deep learning (DL) electroencephalography (EEG) imagined speech signal processing transfer learning (TL) |
| url | https://doi.org/10.1088/2399-6528/ad0197 |
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