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Towards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation
Towards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153026
- ISBN
- 9798346869054
- DDC
- 620
- 서명/저자
- Towards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 283 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Alimohammad, Amir;Mercier, Patrick.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Autonomous brain-computer interfaces (BCIs) are devices designed to record, process, and interpret neural activity, enabling individuals with neurodegenerative diseases or spinal cord injuries to regain their ability to interact with their environment without assistance. Over the past two decades, BCI technology has advanced remarkably, largely due to improvements in neural recording interfaces, such as high-density micro-electrode arrays (MEAs). These innovations allow researchers to collect more comprehensive neural data, enhancing our understanding of neural dynamics and their interaction with human physiology. However, BCI technology still faces three major challenges that hinder its progression toward practical applications.1. Data Overload: Modern MEAs, with their increased number of recording channels, generate vast amounts of data. Consequently, the power consumption required to acquire, process, and transmit this data becomes a significant barrier, especially given tissue-safe design constraints. This dissertation investigates three effective approaches to reduce data rates, and thus power consumption: spike detection, spike sorting, and neural signal compression, all of which leverage relevant signal features for specific applications.2. Neural decoding versatility: No single neural decoding algorithm suits all applications or users. Therefore, a versatile BCI system must be capable of executing a range of neural decoding algorithms. This dissertation examines the efficient design and implementation of two processor architectures supporting various neural decoding schemes. One processor uses fine-grained sequential processing to implement arbitrary machine-learning-based neural decoding models, while the other employs biologically plausible neuron models to realize a spiking neural network.3. Autonomous user engagement: Traditional BCIs require users to engage with the system during predefined time periods. This dissertation explores two methods for estimating a user's intention to engage with a BCI application: one using high-frequency neural spikes and the other using low-frequency local field potentials. A hybrid (multi-signal) asynchronous BCI is designed, implemented, and verified, combining both neural signal types to optimize intention estimation and improve neural decoding performance.The approaches discussed in this dissertation present practical strategies to advance BCI technology by reducing power consumption, enabling flexible and robust neural decoding, and incorporating various neural signal features for efficient user intention estimation.
- 일반주제명
- Engineering
- 일반주제명
- Computer engineering
- 일반주제명
- Electrical engineering
- 키워드
- Digital circuits
- 기타저자
- University of California, San Diego Electrical and Computer Engineering (Joint Doctoral with SDSU)
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798346869054
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aValencia, Daniel.
■24510▼aTowards Autonomous Brain-Computer Interfaces: Approaches, Design, and Implementation
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a283 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Alimohammad, Amir;Mercier, Patrick.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aAutonomous brain-computer interfaces (BCIs) are devices designed to record, process, and interpret neural activity, enabling individuals with neurodegenerative diseases or spinal cord injuries to regain their ability to interact with their environment without assistance. Over the past two decades, BCI technology has advanced remarkably, largely due to improvements in neural recording interfaces, such as high-density micro-electrode arrays (MEAs). These innovations allow researchers to collect more comprehensive neural data, enhancing our understanding of neural dynamics and their interaction with human physiology. However, BCI technology still faces three major challenges that hinder its progression toward practical applications.1. Data Overload: Modern MEAs, with their increased number of recording channels, generate vast amounts of data. Consequently, the power consumption required to acquire, process, and transmit this data becomes a significant barrier, especially given tissue-safe design constraints. This dissertation investigates three effective approaches to reduce data rates, and thus power consumption: spike detection, spike sorting, and neural signal compression, all of which leverage relevant signal features for specific applications.2. Neural decoding versatility: No single neural decoding algorithm suits all applications or users. Therefore, a versatile BCI system must be capable of executing a range of neural decoding algorithms. This dissertation examines the efficient design and implementation of two processor architectures supporting various neural decoding schemes. One processor uses fine-grained sequential processing to implement arbitrary machine-learning-based neural decoding models, while the other employs biologically plausible neuron models to realize a spiking neural network.3. Autonomous user engagement: Traditional BCIs require users to engage with the system during predefined time periods. This dissertation explores two methods for estimating a user's intention to engage with a BCI application: one using high-frequency neural spikes and the other using low-frequency local field potentials. A hybrid (multi-signal) asynchronous BCI is designed, implemented, and verified, combining both neural signal types to optimize intention estimation and improve neural decoding performance.The approaches discussed in this dissertation present practical strategies to advance BCI technology by reducing power consumption, enabling flexible and robust neural decoding, and incorporating various neural signal features for efficient user intention estimation.
■590 ▼aSchool code: 0033.
■650 4▼aEngineering
■650 4▼aComputer engineering
■650 4▼aElectrical engineering
■653 ▼aApplication-specific integrated circuits
■653 ▼aBrain-computer interfaces
■653 ▼aDigital circuits
■653 ▼aNeural signal processing
■690 ▼a0537
■690 ▼a0544
■690 ▼a0464
■71020▼aUniversity of California, San Diego▼bElectrical and Computer Engineering (Joint Doctoral with SDSU).
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164641▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
