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Spatial spiking neural network for classification of EEG signals for concealed information test

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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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Authors and Affiliations

  1. Department of CSE, National Institute of Technology Goa, Veling, Goa, India

    Damoder Reddy Edla

  2. Department of CSE, Indian Institute of Information Technology Sricity, Sathyavedu, Andhra Pradesh, India

    Annushree Bablani

  3. Computer Science, SCEIS, Ulster University, Magee Campus, Londonderry, UK

    Saugat Bhattacharyya

  4. Department of CSE, Indian Institute of Technology (ISM), Dhanbad, Jharkand, India

    Ramesh Dharavath

  5. Department of CSE, National Institute of Technology Warangal, Warangal, Telangana, India

    Ramalingaswamy Cheruku

  6. Department of ECE, National Institute of Technology Warangal, Warangal, Telangana, India

    Vijayasree Boddu

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  1. Damoder Reddy Edla
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Contributions

study conception and design: Damoder Reddy Edla and Dharavath Ramesh Implementation: Anushree Bablani and Ramalingaswamy Cheruku; draft manuscript preparation: Boddu Vijayasree and Saugat Battacharya. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Ramalingaswamy Cheruku.

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Ramalingaswamy Cheruku and Vijayasree Boddu contributed equally to this work.

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Edla, D.R., Bablani, A., Bhattacharyya, S. et al. Spatial spiking neural network for classification of EEG signals for concealed information test. Multimed Tools Appl 83, 79259–79280 (2024). https://doi.org/10.1007/s11042-024-18698-8

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