The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review
<div><p>Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one’s quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues wi...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513519087517696 |
|---|---|
| author | Uzair Shah (15740699) |
| author2 | Mahmood Alzubaidi (15740693) Farida Mohsen (16994682) Alaa Abd-Alrazaq (17430900) Tanvir Alam (638619) Mowafa Househ (9154124) |
| author2_role | author author author author author |
| author_facet | Uzair Shah (15740699) Mahmood Alzubaidi (15740693) Farida Mohsen (16994682) Alaa Abd-Alrazaq (17430900) Tanvir Alam (638619) Mowafa Househ (9154124) |
| author_role | author |
| dc.creator.none.fl_str_mv | Uzair Shah (15740699) Mahmood Alzubaidi (15740693) Farida Mohsen (16994682) Alaa Abd-Alrazaq (17430900) Tanvir Alam (638619) Mowafa Househ (9154124) |
| dc.date.none.fl_str_mv | 2022-09-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s22186975 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_Role_of_Artificial_Intelligence_in_Decoding_Speech_from_EEG_Signals_A_Scoping_Review/25659084 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Biochemistry and cell biology Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware sensors speech decoding electroencephalogram signals artificial intelligence imagine speech |
| dc.title.none.fl_str_mv | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one’s quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22186975" target="_blank">https://dx.doi.org/10.3390/s22186975</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3f3c53baeb7e81d07aab79db223ebb0f |
| identifier_str_mv | 10.3390/s22186975 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25659084 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping ReviewUzair Shah (15740699)Mahmood Alzubaidi (15740693)Farida Mohsen (16994682)Alaa Abd-Alrazaq (17430900)Tanvir Alam (638619)Mowafa Househ (9154124)Biological sciencesBiochemistry and cell biologyChemical sciencesAnalytical chemistryEngineeringElectrical engineeringElectronics, sensors and digital hardwaresensorsspeech decodingelectroencephalogramsignalsartificial intelligenceimagine speech<div><p>Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one’s quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22186975" target="_blank">https://dx.doi.org/10.3390/s22186975</a></p>2022-09-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s22186975https://figshare.com/articles/journal_contribution/The_Role_of_Artificial_Intelligence_in_Decoding_Speech_from_EEG_Signals_A_Scoping_Review/25659084CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256590842022-09-15T03:00:00Z |
| spellingShingle | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review Uzair Shah (15740699) Biological sciences Biochemistry and cell biology Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware sensors speech decoding electroencephalogram signals artificial intelligence imagine speech |
| status_str | publishedVersion |
| title | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| title_full | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| title_fullStr | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| title_full_unstemmed | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| title_short | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| title_sort | The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review |
| topic | Biological sciences Biochemistry and cell biology Chemical sciences Analytical chemistry Engineering Electrical engineering Electronics, sensors and digital hardware sensors speech decoding electroencephalogram signals artificial intelligence imagine speech |