SSVEP and ANN based optimal speller design for Brain Computer Interface Article Swipe
Irshad Ahmad Ansari
,
Rajesh Singla
,
Munendra Singh
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.15181/csat.v2i2.1059
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.15181/csat.v2i2.1059
This work put forwards an optimal BCI (Brain Computer Interface) speller design based on Steady State Visual Evoked Potentials (SSVEP) and Artificial Neural Network (ANN) in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM) of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM. DOI: 10.15181/csat.v2i2.1059
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15181/csat.v2i2.1059
- OA Status
- gold
- Cited By
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1859928033
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1859928033Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.15181/csat.v2i2.1059Digital Object Identifier
- Title
-
SSVEP and ANN based optimal speller design for Brain Computer InterfaceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-07-03Full publication date if available
- Authors
-
Irshad Ahmad Ansari, Rajesh Singla, Munendra SinghList of authors in order
- Landing page
-
https://doi.org/10.15181/csat.v2i2.1059Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.15181/csat.v2i2.1059Direct OA link when available
- Concepts
-
Brain–computer interface, Interface (matter), Computer science, Human–computer interaction, Interface design, Speech recognition, Artificial intelligence, Neuroscience, Electroencephalography, Psychology, Operating system, Maximum bubble pressure method, BubbleTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 3, 2020: 1, 2019: 1, 2016: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.time | 136, 182 |
| abstract_inverted_index.used | 94 |
| abstract_inverted_index.user | 147 |
| abstract_inverted_index.with | 31, 144, 158, 191, 194 |
| abstract_inverted_index.work | 1, 36, 56 |
| abstract_inverted_index.(ANN) | 24 |
| abstract_inverted_index.(CPM) | 153 |
| abstract_inverted_index.94.5% | 163 |
| abstract_inverted_index.State | 15 |
| abstract_inverted_index.based | 12 |
| abstract_inverted_index.first | 166 |
| abstract_inverted_index.gives | 149 |
| abstract_inverted_index.motor | 33 |
| abstract_inverted_index.order | 26, 128 |
| abstract_inverted_index.power | 170 |
| abstract_inverted_index.train | 96 |
| abstract_inverted_index.users | 193 |
| abstract_inverted_index.using | 102, 178 |
| abstract_inverted_index.which | 92 |
| abstract_inverted_index.works | 140 |
| abstract_inverted_index.(Brain | 7 |
| abstract_inverted_index.95.1%. | 199 |
| abstract_inverted_index.Evoked | 17 |
| abstract_inverted_index.First, | 63 |
| abstract_inverted_index.Neural | 22 |
| abstract_inverted_index.Steady | 14 |
| abstract_inverted_index.Visual | 16 |
| abstract_inverted_index.again, | 121 |
| abstract_inverted_index.before | 208 |
| abstract_inverted_index.better | 190 |
| abstract_inverted_index.choose | 70 |
| abstract_inverted_index.design | 11 |
| abstract_inverted_index.enough | 213 |
| abstract_inverted_index.matrix | 115 |
| abstract_inverted_index.minute | 152 |
| abstract_inverted_index.nearly | 141 |
| abstract_inverted_index.neural | 103 |
| abstract_inverted_index.people | 30 |
| abstract_inverted_index.second | 78, 181 |
| abstract_inverted_index.severe | 32 |
| abstract_inverted_index.steps: | 62 |
| abstract_inverted_index.system | 99, 139 |
| abstract_inverted_index.window | 183 |
| abstract_inverted_index.(SSVEP) | 19 |
| abstract_inverted_index.Network | 23 |
| abstract_inverted_index.account | 210 |
| abstract_inverted_index.acquire | 89 |
| abstract_inverted_index.average | 160, 196 |
| abstract_inverted_index.carried | 38 |
| abstract_inverted_index.density | 172 |
| abstract_inverted_index.divided | 59 |
| abstract_inverted_index.enhance | 41 |
| abstract_inverted_index.feature | 85, 175 |
| abstract_inverted_index.focused | 110 |
| abstract_inverted_index.optimal | 5, 90 |
| abstract_inverted_index.speller | 10, 74, 107 |
| abstract_inverted_index.system, | 54 |
| abstract_inverted_index.system. | 49 |
| abstract_inverted_index.vectors | 176 |
| abstract_inverted_index.window. | 137 |
| abstract_inverted_index.Computer | 8 |
| abstract_inverted_index.accuracy | 43, 161, 197, 203 |
| abstract_inverted_index.choosing | 165 |
| abstract_inverted_index.encoding | 67 |
| abstract_inverted_index.features | 133 |
| abstract_inverted_index.forwards | 3 |
| abstract_inverted_index.network. | 104 |
| abstract_inverted_index.optimize | 51, 130 |
| abstract_inverted_index.performs | 189 |
| abstract_inverted_index.reported | 207 |
| abstract_inverted_index.spectral | 171 |
| abstract_inverted_index.user’s | 135 |
| abstract_inverted_index.Optimized | 138, 187 |
| abstract_inverted_index.algorithm | 87 |
| abstract_inverted_index.character | 114, 150 |
| abstract_inverted_index.designing | 64, 118 |
| abstract_inverted_index.features, | 91 |
| abstract_inverted_index.harmonics | 168 |
| abstract_inverted_index.interface | 75, 108 |
| abstract_inverted_index.selection | 131 |
| abstract_inverted_index.technique | 68 |
| abstract_inverted_index.Artificial | 21 |
| abstract_inverted_index.Interface) | 9 |
| abstract_inverted_index.Potentials | 18 |
| abstract_inverted_index.characters | 71 |
| abstract_inverted_index.extraction | 86 |
| abstract_inverted_index.selection. | 186 |
| abstract_inverted_index.development | 81 |
| abstract_inverted_index.experienced | 192 |
| abstract_inverted_index.parameters. | 119 |
| abstract_inverted_index.Optimization | 105 |
| abstract_inverted_index.impairments. | 34 |
| abstract_inverted_index.communication | 45 |
| abstract_inverted_index.deliberations | 125 |
| abstract_inverted_index.classification | 101 |
| abstract_inverted_index.implementation | 83 |
| abstract_inverted_index.representation | 112 |
| abstract_inverted_index.10.15181/csat.v2i2.1059 | 216 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.59914738 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |