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

• Published in: Multimedia tools and applications Vol. 83; no. 33; pp. 79259 - 79280
• Main Authors: Edla, Damoder Reddy, Bablani, Annushree, Bhattacharyya, Saugat, Dharavath, Ramesh, Cheruku, Ramalingaswamy, Boddu, Vijayasree
• Format: Journal Article
• Language: English
• Published: New York Springer US 04.03.2024; Springer Nature B.V
• Subjects: Algorithms; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Electroencephalography; FIR filters; Multimedia Information Systems; Neural networks; Pattern recognition; Signal classification; Spatial data; Special Purpose and Application-Based Systems; Spiking; Electroencephalography; Brain computer interface; Concealed information test; Spiking neural networks
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18698-8

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.