Title: A novel approach for brain thoughts to text conversion using average golden search optimisation and deep recurrent neural network

Authors: Adnan Ahmed; Waseemullah Nazir

Addresses: Department of Computer Science and Information Technology, NED University of Engineering and Technology, Karachi – 75270, Pakistan ' Department of Computer Science and Information Technology, NED University of Engineering and Technology, Karachi – 75270, Pakistan

Abstract: This research proposes a brain signals-to-text converting framework using optimisation-enabled deep learning. Here, conversion is carried out using the brain electroencephalogram (EEG) signals based on the neural activity corresponding to attempted imaginary statements/questions. The EEG signal is subjected to various processes, like signal pre-processing, signal segmentation, feature extraction, character recognition, and language modelling. Here, a deep recurrent neural network (DRNN) is employed to recognise the words in the EEG signals based on the extracted features, and the DRNN is trained using the average golden search optimisation (AGSO) algorithm. Additionally, the successive words or characters are estimated by the usage of a language modelling with the Gaussian mixture model (GMM). The experimental validation of the proposed AGSO-DRNN is compared with other conventional techniques and the proposed model attained a maximum F-measure, MSE, precision, and sensitivity, recall, text conversion accuracy of 0.888, 0.005, 0.917, 0.891 and 0.873 respectively.

Keywords: brain-computer interface; BCI; deep recurrent neural network; DRNN; average golden search optimisation; AGSO; Gaussian mixture model; GMM; electroencephalogram.

DOI: 10.1504/IJAHUC.2025.150220

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.50 No.4, pp.205 - 222

Received: 25 Nov 2023
Accepted: 04 Apr 2025
Published online: 03 Dec 2025 *

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