Spatial spiking neural network for classification of EEG signals for concealed information test
In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in...
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| Published in | Multimedia tools and applications Vol. 83; no. 33; pp. 79259 - 79280 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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New York
Springer US
01.10.2024
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 1573-7721 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-024-18698-8 |
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| Abstract | In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in various domains, including concealed information tests, aimed at detecting deception. This paper introduces the Spatial Spiking Neural Network, a supervised approach for classifying EEG data collected during concealed information tests. Temporal EEG data undergoes filtration using a Finite Impulse Response (FIR) filter, while Common Spatial Pattern (CSP) is employed to extract spatial components. Binary classification is achieved through an integrate-and-fire neuron model, where the frequency of spike generation determines the classification. Spiking Neural Networks (SNNs) offers advantages in terms of temporal precision, event-driven processing, and low power consumption. Their spike-based communication allows for efficient handling of sparse data and recognition of temporal patterns, contributing to robustness and energy efficiency. The proposed model is applied separately to each subject’s EEG data, and the results are compared with traditional classification algorithms. The proposed approach attains a peak accuracy of 90.15%, showcasing superior performance compared to alternative methods. |
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| AbstractList | In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in various domains, including concealed information tests, aimed at detecting deception. This paper introduces the Spatial Spiking Neural Network, a supervised approach for classifying EEG data collected during concealed information tests. Temporal EEG data undergoes filtration using a Finite Impulse Response (FIR) filter, while Common Spatial Pattern (CSP) is employed to extract spatial components. Binary classification is achieved through an integrate-and-fire neuron model, where the frequency of spike generation determines the classification. Spiking Neural Networks (SNNs) offers advantages in terms of temporal precision, event-driven processing, and low power consumption. Their spike-based communication allows for efficient handling of sparse data and recognition of temporal patterns, contributing to robustness and energy efficiency. The proposed model is applied separately to each subject’s EEG data, and the results are compared with traditional classification algorithms. The proposed approach attains a peak accuracy of 90.15%, showcasing superior performance compared to alternative methods. |
| Author | Edla, Damoder Reddy Cheruku, Ramalingaswamy Boddu, Vijayasree Dharavath, Ramesh Bablani, Annushree Bhattacharyya, Saugat |
| Author_xml | – sequence: 1 givenname: Damoder Reddy surname: Edla fullname: Edla, Damoder Reddy organization: Department of CSE, National Institute of Technology Goa – sequence: 2 givenname: Annushree surname: Bablani fullname: Bablani, Annushree organization: Department of CSE, Indian Institute of Information Technology Sricity – sequence: 3 givenname: Saugat surname: Bhattacharyya fullname: Bhattacharyya, Saugat organization: Computer Science, SCEIS, Ulster University, Magee Campus – sequence: 4 givenname: Ramesh surname: Dharavath fullname: Dharavath, Ramesh organization: Department of CSE, Indian Institute of Technology (ISM), Dhanbad – sequence: 5 givenname: Ramalingaswamy orcidid: 0000-0003-1677-5321 surname: Cheruku fullname: Cheruku, Ramalingaswamy email: rmlswamy@nitw.ac.in organization: Department of CSE, National Institute of Technology Warangal – sequence: 6 givenname: Vijayasree surname: Boddu fullname: Boddu, Vijayasree organization: Department of ECE, National Institute of Technology Warangal |
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| Keywords | Electroencephalography Brain computer interface Concealed information test Spiking neural networks |
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