FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces Article Swipe
Pedro R. A. S. Bassi
,
Romis Attux
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/ac6300
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/ac6300
Objective. To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach. We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI. Results. The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins. Conclusion and significance. Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable, economical, fast and low-latency BCIs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2057-1976/ac6300
- OA Status
- green
- Cited By
- 9
- References
- 30
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3211798585Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2057-1976/ac6300Digital Object Identifier
- Title
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FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfacesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-31Full publication date if available
- Authors
-
Pedro R. A. S. Bassi, Romis AttuxList of authors in order
- Landing page
-
https://doi.org/10.1088/2057-1976/ac6300Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.02165Direct OA link when available
- Concepts
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Computer science, Filter (signal processing), Artificial neural network, Brain–computer interface, Artificial intelligence, Neuroscience, Computer vision, Biology, ElectroencephalographyTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3093318272, https://openalex.org/W3063809065, https://openalex.org/W2905523904, https://openalex.org/W2081931753, https://openalex.org/W2098725211, https://openalex.org/W2143183535, https://openalex.org/W4239510810, https://openalex.org/W6751994345, https://openalex.org/W2900362818, https://openalex.org/W2061171222, https://openalex.org/W2590420622, https://openalex.org/W2064675550, https://openalex.org/W3000563309, https://openalex.org/W2553904372, https://openalex.org/W3037233591, https://openalex.org/W3127224190, https://openalex.org/W3198315730, https://openalex.org/W6603207984, https://openalex.org/W3019790736, https://openalex.org/W6638667902, https://openalex.org/W2944495915, https://openalex.org/W2909329242, https://openalex.org/W4231896027, https://openalex.org/W2060160257, https://openalex.org/W80185880, https://openalex.org/W2806884935, https://openalex.org/W2949117887, https://openalex.org/W3149679044, https://openalex.org/W1605417594, https://openalex.org/W1836465849 |
| referenced_works_count | 30 |
| abstract_inverted_index.a | 54, 63, 74, 118, 206 |
| abstract_inverted_index.2D | 64 |
| abstract_inverted_index.3D | 75 |
| abstract_inverted_index.In | 46 |
| abstract_inverted_index.To | 2 |
| abstract_inverted_index.We | 28, 95 |
| abstract_inverted_index.as | 89, 108 |
| abstract_inverted_index.by | 138, 151 |
| abstract_inverted_index.in | 16, 42, 86, 200 |
| abstract_inverted_index.of | 32, 38, 131, 163, 174, 212 |
| abstract_inverted_index.on | 100 |
| abstract_inverted_index.so | 107 |
| abstract_inverted_index.to | 110, 167, 192 |
| abstract_inverted_index.we | 49, 84 |
| abstract_inverted_index.EEG | 40 |
| abstract_inverted_index.The | 122, 197 |
| abstract_inverted_index.and | 13, 18, 73, 104, 141, 149, 156, 185, 216 |
| abstract_inverted_index.for | 92, 209 |
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| abstract_inverted_index.our | 97 |
| abstract_inverted_index.the | 30, 39, 60, 114, 125, 129, 171, 186, 190, 194, 201, 210 |
| abstract_inverted_index.(SVM | 148 |
| abstract_inverted_index.BCI. | 120 |
| abstract_inverted_index.DNNs | 123 |
| abstract_inverted_index.data | 25 |
| abstract_inverted_index.deep | 9, 164 |
| abstract_inverted_index.even | 152 |
| abstract_inverted_index.fast | 215 |
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| abstract_inverted_index.more | 168, 179 |
| abstract_inverted_index.open | 102 |
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| abstract_inverted_index.than | 181 |
| abstract_inverted_index.them | 106 |
| abstract_inverted_index.they | 142 |
| abstract_inverted_index.this | 47, 87, 135 |
| abstract_inverted_index.time | 61 |
| abstract_inverted_index.with | 23, 44, 124 |
| abstract_inverted_index.BCIs. | 218 |
| abstract_inverted_index.CNNs. | 196 |
| abstract_inverted_index.DNNs. | 45 |
| abstract_inverted_index.SSVEP | 5, 93, 145 |
| abstract_inverted_index.allow | 160 |
| abstract_inverted_index.banks | 34, 127, 159 |
| abstract_inverted_index.carry | 178 |
| abstract_inverted_index.final | 115 |
| abstract_inverted_index.input | 91 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.other | 195 |
| abstract_inverted_index.small | 24 |
| abstract_inverted_index.study | 88 |
| abstract_inverted_index.three | 51, 101 |
| abstract_inverted_index.types | 162 |
| abstract_inverted_index.user, | 116 |
| abstract_inverted_index.using | 8 |
| abstract_inverted_index.which | 83 |
| abstract_inverted_index.(BCIs) | 22 |
| abstract_inverted_index.(DNNs) | 12 |
| abstract_inverted_index.FBCCA) | 150 |
| abstract_inverted_index.Filter | 158 |
| abstract_inverted_index.SSVEP. | 175 |
| abstract_inverted_index.common | 144 |
| abstract_inverted_index.filter | 33, 126 |
| abstract_inverted_index.higher | 153 |
| abstract_inverted_index.neural | 10, 56, 66, 77, 98, 165 |
| abstract_inverted_index.strong | 207 |
| abstract_inverted_index.tested | 96 |
| abstract_inverted_index.(FBRNN) | 58 |
| abstract_inverted_index.Complex | 176 |
| abstract_inverted_index.analyze | 170 |
| abstract_inverted_index.complex | 70, 81, 182 |
| abstract_inverted_index.created | 50 |
| abstract_inverted_index.domain, | 62 |
| abstract_inverted_index.improve | 14 |
| abstract_inverted_index.methods | 147 |
| abstract_inverted_index.models: | 53 |
| abstract_inverted_index.network | 57, 67, 78 |
| abstract_inverted_index.propose | 3, 29 |
| abstract_inverted_index.require | 111 |
| abstract_inverted_index.signal) | 41 |
| abstract_inverted_index.similar | 132 |
| abstract_inverted_index.surpass | 193 |
| abstract_inverted_index.without | 134 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.FBCNN-3D | 191 |
| abstract_inverted_index.Results. | 121 |
| abstract_inverted_index.accuracy | 130 |
| abstract_inverted_index.allowing | 189 |
| abstract_inverted_index.context, | 48 |
| abstract_inverted_index.datasets | 103 |
| abstract_inverted_index.features | 72, 184 |
| abstract_inverted_index.harmonic | 172 |
| abstract_inverted_index.lengths. | 26 |
| abstract_inverted_index.margins, | 140 |
| abstract_inverted_index.margins. | 154 |
| abstract_inverted_index.networks | 11, 99, 133, 166 |
| abstract_inverted_index.obtained | 199 |
| abstract_inverted_index.possible | 90 |
| abstract_inverted_index.problems | 204 |
| abstract_inverted_index.spectrum | 71, 183 |
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| abstract_inverted_index.(creating | 35 |
| abstract_inverted_index.Approach. | 27 |
| abstract_inverted_index.analyzing | 59, 80 |
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| abstract_inverted_index.different | 52, 161 |
| abstract_inverted_index.indicates | 205 |
| abstract_inverted_index.introduce | 85 |
| abstract_inverted_index.magnitude | 187 |
| abstract_inverted_index.portable, | 213 |
| abstract_inverted_index.potential | 208 |
| abstract_inverted_index.recurrent | 55 |
| abstract_inverted_index.spectrum, | 188 |
| abstract_inverted_index.surpassed | 128 |
| abstract_inverted_index.(FBCNN-2D) | 68 |
| abstract_inverted_index.(FBCNN-3D) | 79 |
| abstract_inverted_index.Conclusion | 155 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.components | 37, 173 |
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| abstract_inverted_index.challenging | 202 |
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| abstract_inverted_index.economical, | 214 |
| abstract_inverted_index.efficiently | 169 |
| abstract_inverted_index.information | 180 |
| abstract_inverted_index.low-latency | 217 |
| abstract_inverted_index.utilization | 31 |
| abstract_inverted_index.considerable | 139 |
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| abstract_inverted_index.outperformed | 143 |
| abstract_inverted_index.performances | 15, 198 |
| abstract_inverted_index.spectrograms | 177 |
| abstract_inverted_index.convolutional | 65, 76 |
| abstract_inverted_index.methodologies | 7 |
| abstract_inverted_index.preprocessing | 136 |
| abstract_inverted_index.significance. | 157 |
| abstract_inverted_index.spectrograms, | 82 |
| abstract_inverted_index.brain-computer | 20 |
| abstract_inverted_index.classification | 6, 146, 203 |
| abstract_inverted_index.single-channel | 17 |
| abstract_inverted_index.classification. | 94 |
| abstract_inverted_index.user-independent | 19, 119 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.74856306 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |