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Advancing EEG-Based Brain-Computer Interfaces With Real-Time Deep Learning-Based Decoding
Advancing EEG-Based Brain-Computer Interfaces With Real-Time Deep Learning-Based Decoding
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103200
- ISBN
- 9798314865903
- DDC
- 610
- 저자명
- Forenzo, Dylan.
- 서명/저자
- Advancing EEG-Based Brain-Computer Interfaces With Real-Time Deep Learning-Based Decoding
- 발행사항
- [Sl] : Carnegie Mellon University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 127 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: He, Bin.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2025.
- 초록/해제
- 요약Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are devices that allow users to control computers or robotic devices using signals recorded directly from their brains. Since these devices bypass the need for muscle or speech activation, they have the potential to replace or restore motor functions for motor-impaired patients. BCIs may also improve the lives of the general population by providing a direct line of communication with their personal devices and the Internet of Things. EEG signals are recorded non-invasively from outside of the brain, making them a safe option for BCI systems, particularly for users who are not candidates for invasive surgery. However, EEG signals also have relatively low signal-to-noise ratios, poor spatial resolution, and high variability across subjects and sessions, which has so far limited the performance and applications of these devices compared to invasive BCI methods. This thesis aims to improve the performance and reliability of EEG-based BCIs by addressing three of the main components of BCI systems: the control paradigm, the signal processing algorithms, and the end application. Specifically, the results of the three studies included in this work show that integrating several control paradigms can produce multiple EEG feature sets simultaneously, that online deep learning-based decoding can improve performance in continuous control tasks, and that the resulting system can be used for complex tasks involving physical robotic devices. As a culmination of this work, we demonstrate that the proposed EEG BCI system using real-time deep learning-based decoding allows both able-bodied and motor-impaired users to continuously control a robotic arm to pick up, move, and place cups around a set of shelves using only their EEG signals. These studies provide a contribution towards the advancement of EEG-based BCIs and show the potential for these systems to move towards real-world and clinical applications.
- 일반주제명
- Biomedical engineering
- 일반주제명
- Neurosciences
- 일반주제명
- Biomechanics
- 일반주제명
- Clinical psychology
- 키워드
- Deep learning
- 키워드
- Motor Imagery
- 기타저자
- Carnegie Mellon University Biomedical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202103200
■006m o d
■007cr#unu||||||||
■020 ▼a9798314865903
■035 ▼a(MiAaPQ)AAI31998975
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aForenzo, Dylan.▼0(orcid)0000-0002-2661-7434
■24510▼aAdvancing EEG-Based Brain-Computer Interfaces With Real-Time Deep Learning-Based Decoding
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a127 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: He, Bin.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2025.
■520 ▼aElectroencephalography (EEG)-based brain-computer interfaces (BCIs) are devices that allow users to control computers or robotic devices using signals recorded directly from their brains. Since these devices bypass the need for muscle or speech activation, they have the potential to replace or restore motor functions for motor-impaired patients. BCIs may also improve the lives of the general population by providing a direct line of communication with their personal devices and the Internet of Things. EEG signals are recorded non-invasively from outside of the brain, making them a safe option for BCI systems, particularly for users who are not candidates for invasive surgery. However, EEG signals also have relatively low signal-to-noise ratios, poor spatial resolution, and high variability across subjects and sessions, which has so far limited the performance and applications of these devices compared to invasive BCI methods. This thesis aims to improve the performance and reliability of EEG-based BCIs by addressing three of the main components of BCI systems: the control paradigm, the signal processing algorithms, and the end application. Specifically, the results of the three studies included in this work show that integrating several control paradigms can produce multiple EEG feature sets simultaneously, that online deep learning-based decoding can improve performance in continuous control tasks, and that the resulting system can be used for complex tasks involving physical robotic devices. As a culmination of this work, we demonstrate that the proposed EEG BCI system using real-time deep learning-based decoding allows both able-bodied and motor-impaired users to continuously control a robotic arm to pick up, move, and place cups around a set of shelves using only their EEG signals. These studies provide a contribution towards the advancement of EEG-based BCIs and show the potential for these systems to move towards real-world and clinical applications.
■590 ▼aSchool code: 0041.
■650 4▼aBiomedical engineering
■650 4▼aNeurosciences
■650 4▼aBiomechanics
■650 4▼aClinical psychology
■653 ▼aBrain-computer interfaces
■653 ▼aDeep learning
■653 ▼aElectroencephalography
■653 ▼aMotor Imagery
■653 ▼aSpeech activation
■690 ▼a0541
■690 ▼a0317
■690 ▼a0622
■690 ▼a0648
■71020▼aCarnegie Mellon University▼bBiomedical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357280▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
