Title: Tetralet attention enabled modified N-Adam optimised distributed capsule network for lie detection from electroencephalogram signals

Authors: Anand Ashok Ingle; Jayant P. Mehare

Addresses: Department of Computer Science and Engineering, GH Raisoni University, Anjangaon Bari Road, Amravati-444701(MH), India ' Department of Computer Science and Engineering, GH Raisoni University, Anjangaon Bari Road, Amravati-444701(MH), India

Abstract: Lie detection using an electroencephalogram (EEG) signal has received immense attention. A finite impulse response filter preprocesses the input EEG signal from the lie wave's datasets and the frequency split-up process. If a person lies, signal strength increases; if it exceeds a limit, a lie is detected. However, the convolutional methods produce robustness and false positive rates. The Tetralet attention-enabled modified N-Adam optimised distributed capsule network (Tet-MNDCNet) is proposed. A combination of Tetralet attention-enabled modified N-Adam optimised distributed capsule network and the zero-attention mechanism ensures Tetralet accuracy and reliability. The Tetralet attention is focused on selective features, enhancing relevant EEG patterns for improved lie detection accuracy. The Tet-MNDCNet model's performance is robust due to the HarmoniQ spectrum from the pre-processed signal. N-Adam optimiser reduces the gradient descent problem and improves the model's interpretability. The accuracy of the proposed experimental lie detection task of 97.35% for K-fold is 10.

Keywords: distributed capsule network; lie detection; deep learning; modified N-Adam; electroencephalogram; EEG.

DOI: 10.1504/IJBET.2026.153216

International Journal of Biomedical Engineering and Technology, 2026 Vol.50 No.3, pp.181 - 224

Received: 26 May 2025
Accepted: 01 Aug 2025
Published online: 29 Apr 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article