A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG Data Article Swipe
Ravi Kumar Jha
,
Nikola Kasabov
,
Saugat Bhattacharyya
,
Damien Coyle
,
Girijesh Prasad
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6173906/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6173906/v1
The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNN) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum kernel classifier (SNN-QC), SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilizing feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel feature map with high-order nonlinearity, which has outperformed current state-of-the-art feature maps and various machine learning methods in most of the case studies. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its 1 efficacy across multiple binary classifiers. An experimental validation is also performed on an IBM QPU. The results demonstrate that the SNN-QC significantly outperforms the models that use statistical features rather than features extracted from the SNN as SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6173906/v1
- https://www.researchsquare.com/article/rs-6173906/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408693656
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- OpenAlex ID
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https://openalex.org/W4408693656Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6173906/v1Digital Object Identifier
- Title
-
A Hybrid Spiking Neural Network-Quantum Classifier Framework: A Case Study Using EEG DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-21Full publication date if available
- Authors
-
Ravi Kumar Jha, Nikola Kasabov, Saugat Bhattacharyya, Damien Coyle, Girijesh PrasadList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6173906/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6173906/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-6173906/latest.pdfDirect OA link when available
- Concepts
-
Artificial neural network, Computer science, Electroencephalography, Classifier (UML), Artificial intelligence, Quantum, Pattern recognition (psychology), Machine learning, Neuroscience, Psychology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accurate | 245 |
| abstract_inverted_index.approach | 96 |
| abstract_inverted_index.channels | 65 |
| abstract_inverted_index.classify | 114 |
| abstract_inverted_index.combines | 9 |
| abstract_inverted_index.concept. | 129 |
| abstract_inverted_index.conclude | 234 |
| abstract_inverted_index.distinct | 116 |
| abstract_inverted_index.efficacy | 184 |
| abstract_inverted_index.employed | 56 |
| abstract_inverted_index.enhanced | 163, 247 |
| abstract_inverted_index.features | 212, 215 |
| abstract_inverted_index.learning | 29, 149 |
| abstract_inverted_index.movement | 118 |
| abstract_inverted_index.multiple | 64, 186 |
| abstract_inverted_index.networks | 16 |
| abstract_inverted_index.proposed | 44, 104 |
| abstract_inverted_index.recorded | 62 |
| abstract_inverted_index.studies. | 156 |
| abstract_inverted_index.temporal | 225 |
| abstract_inverted_index.(SNN-QC), | 48 |
| abstract_inverted_index.benchmark | 110 |
| abstract_inverted_index.evaluated | 174 |
| abstract_inverted_index.extracted | 77, 216 |
| abstract_inverted_index.framework | 7 |
| abstract_inverted_index.frequency | 73 |
| abstract_inverted_index.introduce | 131 |
| abstract_inverted_index.performed | 194 |
| abstract_inverted_index.potential | 240 |
| abstract_inverted_index.utilizing | 97 |
| abstract_inverted_index.classified | 82 |
| abstract_inverted_index.classifier | 47 |
| abstract_inverted_index.developing | 243 |
| abstract_inverted_index.high-order | 137 |
| abstract_inverted_index.introduces | 3 |
| abstract_inverted_index.processing | 12, 21 |
| abstract_inverted_index.validation | 191 |
| abstract_inverted_index.variables. | 231 |
| abstract_inverted_index.SNN-quantum | 45 |
| abstract_inverted_index.classifier. | 86 |
| abstract_inverted_index.demonstrate | 201 |
| abstract_inverted_index.information | 11, 20, 53, 70 |
| abstract_inverted_index.interaction | 226 |
| abstract_inverted_index.outperforms | 206 |
| abstract_inverted_index.performance | 170 |
| abstract_inverted_index.statistical | 176, 211 |
| abstract_inverted_index.techniques, | 180 |
| abstract_inverted_index.well-suited | 255 |
| abstract_inverted_index.(rate-based) | 69 |
| abstract_inverted_index.Furthermore, | 157 |
| abstract_inverted_index.classifiers. | 188 |
| abstract_inverted_index.demonstrated | 31, 107 |
| abstract_inverted_index.experimental | 190 |
| abstract_inverted_index.highlighted. | 168 |
| abstract_inverted_index.interactions | 60 |
| abstract_inverted_index.outperformed | 141 |
| abstract_inverted_index.computational | 6 |
| abstract_inverted_index.demonstrating | 181 |
| abstract_inverted_index.nonlinearity, | 138 |
| abstract_inverted_index.significantly | 205 |
| abstract_inverted_index.classification | 34, 101, 123 |
| abstract_inverted_index.neuro-inspired | 10 |
| abstract_inverted_index.representation | 54 |
| abstract_inverted_index.Frequency-based | 68 |
| abstract_inverted_index.hyperparameters | 161 |
| abstract_inverted_index.spatio-temporal | 59, 229, 259 |
| abstract_inverted_index.cross-validation | 179 |
| abstract_inverted_index.energy-efficient | 252 |
| abstract_inverted_index.quantum-enhanced | 27 |
| abstract_inverted_index.state-of-the-art | 143 |
| abstract_inverted_index.neuromorphic-quantum | 246 |
| abstract_inverted_index.biologically-inspired, | 254 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.08663067 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |