Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron Article Swipe
Sapto Budi Priyatno
,
Teguh Prakoso
,
Munawar Agus Riyadi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.22441/sinergi.2022.1.008
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.22441/sinergi.2022.1.008
Physical disability due to amputation can affect a person's quality of life due to limited movement in performing daily activities. Bionic hands are used to help someone with an amputation disability. This research developed a bionic hand control based on electroencephalography sensors capable of measuring the brain's bioelectric activity. The classified brain wave was then translated as activity pattern information. The alpha & beta waves were the focus of this work. This study demonstrated a method to extract and classify motor imagery of brainwave activity patterns. The Fast Fourier Transform (FFT) method extracts motor imagery characteristics. The extraction of features is then classified by the Multilayer Perceptron (MLP) method for five classes of bionic hand movement. Testing was conducted with two scenarios. The first test motor imagery without additional movement showed an accuracy of 77.20 %, while the second test motor imagery combined with head movement showed an accuracy of 84.40% for five classes. The system based on motor imagery has been implemented in a bionic hand that shows the applicability of the proposed method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22441/sinergi.2022.1.008
- https://publikasi.mercubuana.ac.id/index.php/sinergi/article/download/11673/5280
- OA Status
- diamond
- Cited By
- 5
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213308938
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213308938Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22441/sinergi.2022.1.008Digital Object Identifier
- Title
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Classification of motor imagery brain wave for bionic hand movement using multilayer perceptronWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-02-01Full publication date if available
- Authors
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Sapto Budi Priyatno, Teguh Prakoso, Munawar Agus RiyadiList of authors in order
- Landing page
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https://doi.org/10.22441/sinergi.2022.1.008Publisher landing page
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https://publikasi.mercubuana.ac.id/index.php/sinergi/article/download/11673/5280Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://publikasi.mercubuana.ac.id/index.php/sinergi/article/download/11673/5280Direct OA link when available
- Concepts
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Motor imagery, Movement (music), Multilayer perceptron, Computer science, Electroencephalography, Artificial intelligence, Fast Fourier transform, Pattern recognition (psychology), Computer vision, Brain–computer interface, Psychology, Artificial neural network, Neuroscience, Acoustics, Physics, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2024: 1, 2023: 4Per-year citation counts (last 5 years)
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22Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.due | 2, 12 |
| abstract_inverted_index.for | 109, 151 |
| abstract_inverted_index.has | 160 |
| abstract_inverted_index.the | 45, 66, 104, 137, 169, 172 |
| abstract_inverted_index.two | 120 |
| abstract_inverted_index.was | 53, 117 |
| abstract_inverted_index.Fast | 87 |
| abstract_inverted_index.This | 31, 71 |
| abstract_inverted_index.been | 161 |
| abstract_inverted_index.beta | 63 |
| abstract_inverted_index.five | 110, 152 |
| abstract_inverted_index.hand | 36, 114, 166 |
| abstract_inverted_index.head | 144 |
| abstract_inverted_index.help | 25 |
| abstract_inverted_index.life | 11 |
| abstract_inverted_index.test | 124, 139 |
| abstract_inverted_index.that | 167 |
| abstract_inverted_index.then | 54, 101 |
| abstract_inverted_index.this | 69 |
| abstract_inverted_index.used | 23 |
| abstract_inverted_index.wave | 52 |
| abstract_inverted_index.were | 65 |
| abstract_inverted_index.with | 27, 119, 143 |
| abstract_inverted_index.& | 62 |
| abstract_inverted_index.(FFT) | 90 |
| abstract_inverted_index.(MLP) | 107 |
| abstract_inverted_index.77.20 | 134 |
| abstract_inverted_index.alpha | 61 |
| abstract_inverted_index.based | 38, 156 |
| abstract_inverted_index.brain | 51 |
| abstract_inverted_index.daily | 18 |
| abstract_inverted_index.first | 123 |
| abstract_inverted_index.focus | 67 |
| abstract_inverted_index.hands | 21 |
| abstract_inverted_index.motor | 80, 93, 125, 140, 158 |
| abstract_inverted_index.shows | 168 |
| abstract_inverted_index.study | 72 |
| abstract_inverted_index.waves | 64 |
| abstract_inverted_index.while | 136 |
| abstract_inverted_index.work. | 70 |
| abstract_inverted_index.84.40% | 150 |
| abstract_inverted_index.Bionic | 20 |
| abstract_inverted_index.affect | 6 |
| abstract_inverted_index.bionic | 35, 113, 165 |
| abstract_inverted_index.method | 75, 91, 108 |
| abstract_inverted_index.second | 138 |
| abstract_inverted_index.showed | 130, 146 |
| abstract_inverted_index.system | 155 |
| abstract_inverted_index.Fourier | 88 |
| abstract_inverted_index.Testing | 116 |
| abstract_inverted_index.brain's | 46 |
| abstract_inverted_index.capable | 42 |
| abstract_inverted_index.classes | 111 |
| abstract_inverted_index.control | 37 |
| abstract_inverted_index.extract | 77 |
| abstract_inverted_index.imagery | 81, 94, 126, 141, 159 |
| abstract_inverted_index.limited | 14 |
| abstract_inverted_index.method. | 174 |
| abstract_inverted_index.pattern | 58 |
| abstract_inverted_index.quality | 9 |
| abstract_inverted_index.sensors | 41 |
| abstract_inverted_index.someone | 26 |
| abstract_inverted_index.without | 127 |
| abstract_inverted_index.Physical | 0 |
| abstract_inverted_index.accuracy | 132, 148 |
| abstract_inverted_index.activity | 57, 84 |
| abstract_inverted_index.classes. | 153 |
| abstract_inverted_index.classify | 79 |
| abstract_inverted_index.combined | 142 |
| abstract_inverted_index.extracts | 92 |
| abstract_inverted_index.features | 99 |
| abstract_inverted_index.movement | 15, 129, 145 |
| abstract_inverted_index.person's | 8 |
| abstract_inverted_index.proposed | 173 |
| abstract_inverted_index.research | 32 |
| abstract_inverted_index.Transform | 89 |
| abstract_inverted_index.activity. | 48 |
| abstract_inverted_index.brainwave | 83 |
| abstract_inverted_index.conducted | 118 |
| abstract_inverted_index.developed | 33 |
| abstract_inverted_index.measuring | 44 |
| abstract_inverted_index.movement. | 115 |
| abstract_inverted_index.patterns. | 85 |
| abstract_inverted_index.Multilayer | 105 |
| abstract_inverted_index.Perceptron | 106 |
| abstract_inverted_index.additional | 128 |
| abstract_inverted_index.amputation | 4, 29 |
| abstract_inverted_index.classified | 50, 102 |
| abstract_inverted_index.disability | 1 |
| abstract_inverted_index.extraction | 97 |
| abstract_inverted_index.performing | 17 |
| abstract_inverted_index.scenarios. | 121 |
| abstract_inverted_index.translated | 55 |
| abstract_inverted_index.activities. | 19 |
| abstract_inverted_index.bioelectric | 47 |
| abstract_inverted_index.disability. | 30 |
| abstract_inverted_index.implemented | 162 |
| abstract_inverted_index.demonstrated | 73 |
| abstract_inverted_index.information. | 59 |
| abstract_inverted_index.applicability | 170 |
| abstract_inverted_index.characteristics. | 95 |
| abstract_inverted_index.electroencephalography | 40 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.63894452 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |