High-density neural recording system design
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Han-Sol | - |
dc.contributor.author | Eom, Kyeongho | - |
dc.contributor.author | Park, Minju | - |
dc.contributor.author | Ku, Seung-Beom | - |
dc.contributor.author | Lee, Kwonhong | - |
dc.contributor.author | Lee, Hyung-Min | - |
dc.date.accessioned | 2022-08-12T08:40:49Z | - |
dc.date.available | 2022-08-12T08:40:49Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.issn | 2093-9868 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/142887 | - |
dc.description.abstract | Implantable medical devices capable of monitoring hundreds to thousands of electrodes have received great attention in biomedical applications for understanding of the brain function and to treat brain diseases such as epilepsy, dystonia, and Parkinson's disease. Non-invasive neural recording modalities such as fMRI and EEGs were widely used since the 1960s, but to acquire better information, invasive modalities gained popularity. Since such invasive neural recording system requires high efficiency and low power operation, they have been implemented as integrated circuits. Many techniques have been developed and applied when designing integrated high-density neural recording architecture for better performance, higher efficiency, and lower power consumption. This paper covers general knowledge of neural signals and frequently used neural recording architectures for monitoring neural activity. For neural recording architecture, various neural recording amplifier structures are covered. In addition, several neural processing techniques, which can optimize the neural recording system, are also discussed. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGERNATURE | - |
dc.subject | NONLINEAR ENERGY OPERATOR | - |
dc.subject | EEG ACQUISITION SOC | - |
dc.subject | ANALOG FRONT-END | - |
dc.subject | MU-W | - |
dc.subject | INSTRUMENTATION AMPLIFIER | - |
dc.subject | MICROELECTRODE ARRAY | - |
dc.subject | FEATURE-EXTRACTION | - |
dc.subject | CLOSED-LOOP | - |
dc.subject | INTERFACE SYSTEM | - |
dc.subject | SPIKE-DETECTION | - |
dc.title | High-density neural recording system design | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Hyung-Min | - |
dc.identifier.doi | 10.1007/s13534-022-00233-z | - |
dc.identifier.scopusid | 2-s2.0-85131093474 | - |
dc.identifier.wosid | 000802903700001 | - |
dc.identifier.bibliographicCitation | BIOMEDICAL ENGINEERING LETTERS, v.12, no.3, pp.251 - 261 | - |
dc.relation.isPartOf | BIOMEDICAL ENGINEERING LETTERS | - |
dc.citation.title | BIOMEDICAL ENGINEERING LETTERS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 251 | - |
dc.citation.endPage | 261 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordPlus | NONLINEAR ENERGY OPERATOR | - |
dc.subject.keywordPlus | EEG ACQUISITION SOC | - |
dc.subject.keywordPlus | ANALOG FRONT-END | - |
dc.subject.keywordPlus | MU-W | - |
dc.subject.keywordPlus | INSTRUMENTATION AMPLIFIER | - |
dc.subject.keywordPlus | MICROELECTRODE ARRAY | - |
dc.subject.keywordPlus | FEATURE-EXTRACTION | - |
dc.subject.keywordPlus | CLOSED-LOOP | - |
dc.subject.keywordPlus | INTERFACE SYSTEM | - |
dc.subject.keywordPlus | SPIKE-DETECTION | - |
dc.subject.keywordAuthor | High-density | - |
dc.subject.keywordAuthor | Neural recording | - |
dc.subject.keywordAuthor | Neural processing | - |
dc.subject.keywordAuthor | Neural signal | - |
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