A Real-Time Machine Learning Module for Motion Artifact Detection in fNIRS Article Swipe
Renas Ercan
,
Yunjia Xia
,
Yunyi Zhao
,
Rui Loureiro
,
Shufan Yang
,
Hubin Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/iscas58744.2024.10557996
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/iscas58744.2024.10557996
Functional Near-Infrared Spectroscopy (fNIRS) is a neuroimaging method which can be implemented with a wearable form factor. However, the data of fNIRS can be \naffected by motion artifact, which is conventionally processed \noffline using MATLAB-based software package via a bulky PC. \nThis study trains a Support Vector Machine (SVM) algorithm and proposes a hardware design approach based on an FPGA to achieve the first real-time fNIRS motion artifact detection. \nThe SVM hardware architecture proposed here utilizes a partially sequential–partially parallel implementation of the classification algorithm where Support Vector channels are \nconsolidated into a single oversampled channel. A high \nclassification accuracy of 97.42%, low FPGA resource utilization of 38,354 look-up tables and 6024 flip-flops with 10.92 us latency is achieved, outperforming conventional CPU SVM \nmethods. These results show that an FPGA-based fNIRS motion \nartifact detector can be exploited whilst meeting real-time and \nresource constraints that are crucial in high-performance \nreconfigurable hardware systems.
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- en
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- http://doi.org/10.1109/iscas58744.2024.10557996
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- green
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All OpenAlex metadata
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https://openalex.org/W4400233214Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/iscas58744.2024.10557996Digital Object Identifier
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A Real-Time Machine Learning Module for Motion Artifact Detection in fNIRSWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-05-19Full publication date if available
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Renas Ercan, Yunjia Xia, Yunyi Zhao, Rui Loureiro, Shufan Yang, Hubin ZhaoList of authors in order
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https://doi.org/10.1109/iscas58744.2024.10557996Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://discovery.ucl.ac.uk/10187348/1/Final%20Renas%20ISCAS%20draft%20final%20submission%20V2%200129%20YX.pdfDirect OA link when available
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Artifact (error), Computer science, Computer vision, Artificial intelligence, Motion (physics)Top concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.single | 88 |
| abstract_inverted_index.tables | 103 |
| abstract_inverted_index.trains | 40 |
| abstract_inverted_index.whilst | 129 |
| abstract_inverted_index.(fNIRS) | 3 |
| abstract_inverted_index.97.42%, | 95 |
| abstract_inverted_index.Machine | 44 |
| abstract_inverted_index.Support | 42, 82 |
| abstract_inverted_index.achieve | 58 |
| abstract_inverted_index.crucial | 136 |
| abstract_inverted_index.factor. | 16 |
| abstract_inverted_index.latency | 110 |
| abstract_inverted_index.look-up | 102 |
| abstract_inverted_index.meeting | 130 |
| abstract_inverted_index.package | 34 |
| abstract_inverted_index.results | 118 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.accuracy | 93 |
| abstract_inverted_index.approach | 52 |
| abstract_inverted_index.artifact | 64 |
| abstract_inverted_index.channel. | 90 |
| abstract_inverted_index.channels | 84 |
| abstract_inverted_index.detector | 125 |
| abstract_inverted_index.hardware | 50, 67, 139 |
| abstract_inverted_index.parallel | 75 |
| abstract_inverted_index.proposed | 69 |
| abstract_inverted_index.proposes | 48 |
| abstract_inverted_index.resource | 98 |
| abstract_inverted_index.software | 33 |
| abstract_inverted_index.systems. | 140 |
| abstract_inverted_index.utilizes | 71 |
| abstract_inverted_index.wearable | 14 |
| abstract_inverted_index.achieved, | 112 |
| abstract_inverted_index.algorithm | 46, 80 |
| abstract_inverted_index.artifact, | 26 |
| abstract_inverted_index.exploited | 128 |
| abstract_inverted_index.partially | 73 |
| abstract_inverted_index.real-time | 61, 131 |
| abstract_inverted_index.FPGA-based | 122 |
| abstract_inverted_index.Functional | 0 |
| abstract_inverted_index.flip-flops | 106 |
| abstract_inverted_index.constraints | 133 |
| abstract_inverted_index.implemented | 11 |
| abstract_inverted_index.oversampled | 89 |
| abstract_inverted_index.utilization | 99 |
| abstract_inverted_index.MATLAB-based | 32 |
| abstract_inverted_index.Spectroscopy | 2 |
| abstract_inverted_index.architecture | 68 |
| abstract_inverted_index.conventional | 114 |
| abstract_inverted_index.neuroimaging | 6 |
| abstract_inverted_index.Near-Infrared | 1 |
| abstract_inverted_index.outperforming | 113 |
| abstract_inverted_index.PC. \nThis | 38 |
| abstract_inverted_index.classification | 79 |
| abstract_inverted_index.conventionally | 29 |
| abstract_inverted_index.implementation | 76 |
| abstract_inverted_index.be \naffected | 23 |
| abstract_inverted_index.SVM \nmethods. | 116 |
| abstract_inverted_index.and \nresource | 132 |
| abstract_inverted_index.detection. \nThe | 65 |
| abstract_inverted_index.motion \nartifact | 124 |
| abstract_inverted_index.are \nconsolidated | 85 |
| abstract_inverted_index.sequential–partially | 74 |
| abstract_inverted_index.processed \noffline | 30 |
| abstract_inverted_index.high \nclassification | 92 |
| abstract_inverted_index.high-performance \nreconfigurable | 138 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.66188548 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |