Real-Time Programmable Closed-Loop Stimulation/Recording Platforms for Deep Brain Study View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Hung-Chih Chiu , Hsi-Pin Ma

ABSTRACT

Biomedical systems have expanded markedly in recent years, spreading into many areas of human life. Rapid advances in biological science have led to the creation of novel electrical circuits and signal processing methods and the development of tools for diagnosing and treating human diseases. Many biomedical engineering researchers have developed novel tools designed to tackle specific medical conditions. The instruments used represent an interface between biology and electronics. These interfaces enable biological phenomena to be quantified and characterized, thus allowing the biological processes underlying them to be elucidated. A typical interface comprises a sensor or electrode for detecting some biological parameter, the signals from which are then amplified and converted into a digital form. These digital data can be processed by hardware or transferred to a personal computer for closed-loop control, long-term storage, and more precise signal processing. The guidelines for such signal processing algorithms require low complexity, short latency, high sensitivity, and accurate characterization. Microprocessors are used to make the design of an electronic algorithm flexible and adaptable. Depending on the requirements of a specific application, the data can be transferred through wired or wireless links. Communication can be achieved using widely available and clearly defined technical specifications. This chapter discusses the main hardware and software components used in closed-loop deep brain stimulation systems and describes the evaluation procedures that are used to ensure that the system performs as specified. Even when the system parameters can change with the physiological characteristics, a closed-loop control system can accurately extract the signals of interest. More... »

PAGES

237-264

Book

TITLE

Smart Sensors and Systems

ISBN

978-3-319-33200-0
978-3-319-33201-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-33201-7_10

DOI

http://dx.doi.org/10.1007/978-3-319-33201-7_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022954590


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0906", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Electrical and Electronic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Electrical Engineering, National Tsing Hua University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chiu", 
        "givenName": "Hung-Chih", 
        "id": "sg:person.015570501753.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015570501753.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Electrical Engineering, National Tsing Hua University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Hsi-Pin", 
        "id": "sg:person.010134347425.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134347425.24"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.4061/2011/414682", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002527768"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.21736", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004302203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1517/14656566.4.10.1747", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004452007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/275107.275139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004968269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/brain/awh480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010560416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2005.02.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011709784"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2005.02.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011709784"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.23482", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013748413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1528-1167.2010.02536.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014049063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1528-1167.2010.02536.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014049063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1741-2560/8/3/036018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014525928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1235-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014755320", 
          "https://doi.org/10.1007/978-1-4615-1235-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1235-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014755320", 
          "https://doi.org/10.1007/978-1-4615-1235-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm200111083451915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015318908"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cmpb.2012.03.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016536310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mayocp.2014.02.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022449971"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1077603.1077680", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023495312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.5459-09.2010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024098635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jneumeth.2012.05.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024214757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jneumeth.2012.05.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024214757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.0131-09.2009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024567391"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnsre.2010.2081377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025512451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1741-2560/6/1/012001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027724587"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0102576", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028076410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ana.23951", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028685142"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.00724.2009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029911308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2011.08.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033439680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2011.08.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033439680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm200002173420703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035404105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jpsychires.2009.12.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035584196"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/brain/awh411", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036625692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-69960-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036803405", 
          "https://doi.org/10.1007/978-3-540-69960-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-69960-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036803405", 
          "https://doi.org/10.1007/978-3-540-69960-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa000827", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039115421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fncir.2012.00117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040918922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.brs.2014.07.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041496360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep05963", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044939189", 
          "https://doi.org/10.1038/srep05963"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1528-1157.1983.tb04915.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045318601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.expneurol.2012.09.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045516862"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.parkreldis.2010.12.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048105173"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.10143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050215243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/j.nurt.2007.10.065", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052791700", 
          "https://doi.org/10.1016/j.nurt.2007.10.065"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.2010.217489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053345564"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/brain/awh616", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053392454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/brain/awh616", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053392454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.2915137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057883031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/10.821734", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061085597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jetcas.2011.2174472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061280348"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jrproc.1959.287156", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061314703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jssc.2008.2006460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061330058"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jssc.2010.2042245", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061330568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jssc.2011.2108910", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061330820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbcas.2015.2403282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061523095"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2004.827931", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061526167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2008.2005944", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061527351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tcsi.2013.2246251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061567649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2003.99.3.0489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071100897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2003.99.3.0489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071100897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2003.99.3.0489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071100897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2003.99.3.0489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071100897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.1998.79.2.1017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083211175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ner.2013.6696014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094586487"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cicc.2007.4405694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095351082"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/vldi-dat.2013.6533846", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095818296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2006.260193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096110527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2006.260193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096110527"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017", 
    "datePublishedReg": "2017-01-01", 
    "description": "Biomedical systems have expanded markedly in recent years, spreading into many areas of human life. Rapid advances in biological science have led to the creation of novel electrical circuits and signal processing methods and the development of tools for diagnosing and treating human diseases. Many biomedical engineering researchers have developed novel tools designed to tackle specific medical conditions. The instruments used represent an interface between biology and electronics. These interfaces enable biological phenomena to be quantified and characterized, thus allowing the biological processes underlying them to be elucidated. A typical interface comprises a sensor or electrode for detecting some biological parameter, the signals from which are then amplified and converted into a digital form. These digital data can be processed by hardware or transferred to a personal computer for closed-loop control, long-term storage, and more precise signal processing. The guidelines for such signal processing algorithms require low complexity, short latency, high sensitivity, and accurate characterization. Microprocessors are used to make the design of an electronic algorithm flexible and adaptable. Depending on the requirements of a specific application, the data can be transferred through wired or wireless links. Communication can be achieved using widely available and clearly defined technical specifications. This chapter discusses the main hardware and software components used in closed-loop deep brain stimulation systems and describes the evaluation procedures that are used to ensure that the system performs as specified. Even when the system parameters can change with the physiological characteristics, a closed-loop control system can accurately extract the signals of interest.", 
    "editor": [
      {
        "familyName": "Kyung", 
        "givenName": "Chong-Min", 
        "type": "Person"
      }, 
      {
        "familyName": "Yasuura", 
        "givenName": "Hiroto", 
        "type": "Person"
      }, 
      {
        "familyName": "Liu", 
        "givenName": "Yongpan", 
        "type": "Person"
      }, 
      {
        "familyName": "Lin", 
        "givenName": "Youn-Long", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-33201-7_10", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-33200-0", 
        "978-3-319-33201-7"
      ], 
      "name": "Smart Sensors and Systems", 
      "type": "Book"
    }, 
    "name": "Real-Time Programmable Closed-Loop Stimulation/Recording Platforms for Deep Brain Study", 
    "pagination": "237-264", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-33201-7_10"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6d5c4ef0ae53c50fc30a33575f92d2d45a5c098e02099f5660e7e1486a916972"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022954590"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-33201-7_10", 
      "https://app.dimensions.ai/details/publication/pub.1022954590"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T15:26", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000287.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-33201-7_10"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-33201-7_10'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-33201-7_10'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-33201-7_10'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-33201-7_10'


 

This table displays all metadata directly associated to this object as RDF triples.

256 TRIPLES      23 PREDICATES      82 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-33201-7_10 schema:about anzsrc-for:09
2 anzsrc-for:0906
3 schema:author N1ae5c69da2a3483a970829a4c59a3a0c
4 schema:citation sg:pub.10.1007/978-1-4615-1235-6
5 sg:pub.10.1007/978-3-540-69960-6
6 sg:pub.10.1016/j.nurt.2007.10.065
7 sg:pub.10.1038/srep05963
8 https://doi.org/10.1002/ana.23951
9 https://doi.org/10.1002/mds.10143
10 https://doi.org/10.1002/mds.21736
11 https://doi.org/10.1002/mds.23482
12 https://doi.org/10.1016/j.brs.2014.07.039
13 https://doi.org/10.1016/j.cmpb.2012.03.011
14 https://doi.org/10.1016/j.expneurol.2012.09.013
15 https://doi.org/10.1016/j.jneumeth.2012.05.028
16 https://doi.org/10.1016/j.jpsychires.2009.12.010
17 https://doi.org/10.1016/j.mayocp.2014.02.003
18 https://doi.org/10.1016/j.neuron.2005.02.014
19 https://doi.org/10.1016/j.neuron.2011.08.023
20 https://doi.org/10.1016/j.parkreldis.2010.12.005
21 https://doi.org/10.1056/nejm200002173420703
22 https://doi.org/10.1056/nejm200111083451915
23 https://doi.org/10.1056/nejmoa000827
24 https://doi.org/10.1063/1.2915137
25 https://doi.org/10.1088/1741-2560/6/1/012001
26 https://doi.org/10.1088/1741-2560/8/3/036018
27 https://doi.org/10.1093/brain/awh411
28 https://doi.org/10.1093/brain/awh480
29 https://doi.org/10.1093/brain/awh616
30 https://doi.org/10.1109/10.821734
31 https://doi.org/10.1109/cicc.2007.4405694
32 https://doi.org/10.1109/iembs.2006.260193
33 https://doi.org/10.1109/jetcas.2011.2174472
34 https://doi.org/10.1109/jrproc.1959.287156
35 https://doi.org/10.1109/jssc.2008.2006460
36 https://doi.org/10.1109/jssc.2010.2042245
37 https://doi.org/10.1109/jssc.2011.2108910
38 https://doi.org/10.1109/ner.2013.6696014
39 https://doi.org/10.1109/tbcas.2015.2403282
40 https://doi.org/10.1109/tbme.2004.827931
41 https://doi.org/10.1109/tbme.2008.2005944
42 https://doi.org/10.1109/tcsi.2013.2246251
43 https://doi.org/10.1109/tnsre.2010.2081377
44 https://doi.org/10.1109/vldi-dat.2013.6533846
45 https://doi.org/10.1111/j.1528-1157.1983.tb04915.x
46 https://doi.org/10.1111/j.1528-1167.2010.02536.x
47 https://doi.org/10.1136/jnnp.2010.217489
48 https://doi.org/10.1145/1077603.1077680
49 https://doi.org/10.1145/275107.275139
50 https://doi.org/10.1152/jn.00724.2009
51 https://doi.org/10.1152/jn.1998.79.2.1017
52 https://doi.org/10.1371/journal.pone.0102576
53 https://doi.org/10.1517/14656566.4.10.1747
54 https://doi.org/10.1523/jneurosci.0131-09.2009
55 https://doi.org/10.1523/jneurosci.5459-09.2010
56 https://doi.org/10.3171/jns.2003.99.3.0489
57 https://doi.org/10.3389/fncir.2012.00117
58 https://doi.org/10.4061/2011/414682
59 schema:datePublished 2017
60 schema:datePublishedReg 2017-01-01
61 schema:description Biomedical systems have expanded markedly in recent years, spreading into many areas of human life. Rapid advances in biological science have led to the creation of novel electrical circuits and signal processing methods and the development of tools for diagnosing and treating human diseases. Many biomedical engineering researchers have developed novel tools designed to tackle specific medical conditions. The instruments used represent an interface between biology and electronics. These interfaces enable biological phenomena to be quantified and characterized, thus allowing the biological processes underlying them to be elucidated. A typical interface comprises a sensor or electrode for detecting some biological parameter, the signals from which are then amplified and converted into a digital form. These digital data can be processed by hardware or transferred to a personal computer for closed-loop control, long-term storage, and more precise signal processing. The guidelines for such signal processing algorithms require low complexity, short latency, high sensitivity, and accurate characterization. Microprocessors are used to make the design of an electronic algorithm flexible and adaptable. Depending on the requirements of a specific application, the data can be transferred through wired or wireless links. Communication can be achieved using widely available and clearly defined technical specifications. This chapter discusses the main hardware and software components used in closed-loop deep brain stimulation systems and describes the evaluation procedures that are used to ensure that the system performs as specified. Even when the system parameters can change with the physiological characteristics, a closed-loop control system can accurately extract the signals of interest.
62 schema:editor N1044a054c927467492ea3b7349bd2f8c
63 schema:genre chapter
64 schema:inLanguage en
65 schema:isAccessibleForFree false
66 schema:isPartOf Nad0abcf1c9b240bd98c70a4dffe0ac15
67 schema:name Real-Time Programmable Closed-Loop Stimulation/Recording Platforms for Deep Brain Study
68 schema:pagination 237-264
69 schema:productId N53a5b96175e94642bfb3aa1f9732bd9a
70 N5c0f53eacaea4e1199fd881a1924be68
71 N5cdcb455d9174c62b147217d0d925457
72 schema:publisher N3773b72948b74b0eaa2ea84d407d0987
73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022954590
74 https://doi.org/10.1007/978-3-319-33201-7_10
75 schema:sdDatePublished 2019-04-15T15:26
76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
77 schema:sdPublisher N4a5b2aa2445b4f20991d8c0c58f01ea9
78 schema:url http://link.springer.com/10.1007/978-3-319-33201-7_10
79 sgo:license sg:explorer/license/
80 sgo:sdDataset chapters
81 rdf:type schema:Chapter
82 N1044a054c927467492ea3b7349bd2f8c rdf:first Ncb7ae522adcf4670a2280f5bf3c533fc
83 rdf:rest N8fba45bf351946ed9c20018001c3f11a
84 N1ae5c69da2a3483a970829a4c59a3a0c rdf:first sg:person.015570501753.09
85 rdf:rest N5643d7e7fca44ad2a6719181f51472c8
86 N1ec55b809590468abbe0b36e5f0bce71 rdf:first N30bb63f2834b4cb9bcaa0e753f97760f
87 rdf:rest rdf:nil
88 N30bb63f2834b4cb9bcaa0e753f97760f schema:familyName Lin
89 schema:givenName Youn-Long
90 rdf:type schema:Person
91 N3773b72948b74b0eaa2ea84d407d0987 schema:location Cham
92 schema:name Springer International Publishing
93 rdf:type schema:Organisation
94 N4a5b2aa2445b4f20991d8c0c58f01ea9 schema:name Springer Nature - SN SciGraph project
95 rdf:type schema:Organization
96 N53a5b96175e94642bfb3aa1f9732bd9a schema:name readcube_id
97 schema:value 6d5c4ef0ae53c50fc30a33575f92d2d45a5c098e02099f5660e7e1486a916972
98 rdf:type schema:PropertyValue
99 N5643d7e7fca44ad2a6719181f51472c8 rdf:first sg:person.010134347425.24
100 rdf:rest rdf:nil
101 N5c0f53eacaea4e1199fd881a1924be68 schema:name doi
102 schema:value 10.1007/978-3-319-33201-7_10
103 rdf:type schema:PropertyValue
104 N5cdcb455d9174c62b147217d0d925457 schema:name dimensions_id
105 schema:value pub.1022954590
106 rdf:type schema:PropertyValue
107 N8fba45bf351946ed9c20018001c3f11a rdf:first Nfe4f1a9f8db64178ad8a40eaa131b8aa
108 rdf:rest Ne16bbaadbe9948aea9e4f681abdcedc6
109 Nad0abcf1c9b240bd98c70a4dffe0ac15 schema:isbn 978-3-319-33200-0
110 978-3-319-33201-7
111 schema:name Smart Sensors and Systems
112 rdf:type schema:Book
113 Nadca96ea492144f9a0f2763e9de994f9 schema:familyName Liu
114 schema:givenName Yongpan
115 rdf:type schema:Person
116 Ncb7ae522adcf4670a2280f5bf3c533fc schema:familyName Kyung
117 schema:givenName Chong-Min
118 rdf:type schema:Person
119 Ne16bbaadbe9948aea9e4f681abdcedc6 rdf:first Nadca96ea492144f9a0f2763e9de994f9
120 rdf:rest N1ec55b809590468abbe0b36e5f0bce71
121 Nfe4f1a9f8db64178ad8a40eaa131b8aa schema:familyName Yasuura
122 schema:givenName Hiroto
123 rdf:type schema:Person
124 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
125 schema:name Engineering
126 rdf:type schema:DefinedTerm
127 anzsrc-for:0906 schema:inDefinedTermSet anzsrc-for:
128 schema:name Electrical and Electronic Engineering
129 rdf:type schema:DefinedTerm
130 sg:person.010134347425.24 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
131 schema:familyName Ma
132 schema:givenName Hsi-Pin
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010134347425.24
134 rdf:type schema:Person
135 sg:person.015570501753.09 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
136 schema:familyName Chiu
137 schema:givenName Hung-Chih
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015570501753.09
139 rdf:type schema:Person
140 sg:pub.10.1007/978-1-4615-1235-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014755320
141 https://doi.org/10.1007/978-1-4615-1235-6
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/978-3-540-69960-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036803405
144 https://doi.org/10.1007/978-3-540-69960-6
145 rdf:type schema:CreativeWork
146 sg:pub.10.1016/j.nurt.2007.10.065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052791700
147 https://doi.org/10.1016/j.nurt.2007.10.065
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/srep05963 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044939189
150 https://doi.org/10.1038/srep05963
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1002/ana.23951 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028685142
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1002/mds.10143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050215243
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1002/mds.21736 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004302203
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1002/mds.23482 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013748413
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.brs.2014.07.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041496360
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.cmpb.2012.03.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016536310
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.expneurol.2012.09.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045516862
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.jneumeth.2012.05.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024214757
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.jpsychires.2009.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035584196
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.mayocp.2014.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022449971
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.neuron.2005.02.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011709784
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.neuron.2011.08.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033439680
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.parkreldis.2010.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048105173
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1056/nejm200002173420703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035404105
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1056/nejm200111083451915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015318908
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1056/nejmoa000827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039115421
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1063/1.2915137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057883031
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1088/1741-2560/6/1/012001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027724587
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1088/1741-2560/8/3/036018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014525928
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1093/brain/awh411 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036625692
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1093/brain/awh480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010560416
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1093/brain/awh616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053392454
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1109/10.821734 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061085597
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1109/cicc.2007.4405694 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095351082
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1109/iembs.2006.260193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1096110527
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1109/jetcas.2011.2174472 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061280348
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1109/jrproc.1959.287156 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061314703
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1109/jssc.2008.2006460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061330058
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1109/jssc.2010.2042245 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061330568
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1109/jssc.2011.2108910 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061330820
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1109/ner.2013.6696014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094586487
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1109/tbcas.2015.2403282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061523095
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1109/tbme.2004.827931 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061526167
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1109/tbme.2008.2005944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061527351
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1109/tcsi.2013.2246251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061567649
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1109/tnsre.2010.2081377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025512451
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1109/vldi-dat.2013.6533846 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095818296
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1111/j.1528-1157.1983.tb04915.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1045318601
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1111/j.1528-1167.2010.02536.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014049063
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1136/jnnp.2010.217489 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053345564
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1145/1077603.1077680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023495312
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1145/275107.275139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004968269
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1152/jn.00724.2009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029911308
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1152/jn.1998.79.2.1017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083211175
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1371/journal.pone.0102576 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028076410
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1517/14656566.4.10.1747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004452007
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1523/jneurosci.0131-09.2009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024567391
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1523/jneurosci.5459-09.2010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024098635
247 rdf:type schema:CreativeWork
248 https://doi.org/10.3171/jns.2003.99.3.0489 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071100897
249 rdf:type schema:CreativeWork
250 https://doi.org/10.3389/fncir.2012.00117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040918922
251 rdf:type schema:CreativeWork
252 https://doi.org/10.4061/2011/414682 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002527768
253 rdf:type schema:CreativeWork
254 https://www.grid.ac/institutes/grid.38348.34 schema:alternateName National Tsing Hua University
255 schema:name Department of Electrical Engineering, National Tsing Hua University
256 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...