Examining Cognitive Shifts Through EEG: Insights from Resting State to Neurofeedback Game Engagement

Saikat Gochhait; Irina Leonova; Prabha Kiran; Ayodeji Olalekan Salau; Aitizaz Ali; Tin Tin Ting

doi:10.47852/bonviewJCCE52025505

Authors

Saikat Gochhait Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), India and Samara State Medical University, Russia https://orcid.org/0000-0003-4583-9208
Irina Leonova Department of Field and Applied Sociology Vice Dean, Lobachevsky State University of Nizhny Novgorod, Russia
Prabha Kiran Westminster International University in Tashkent, Uzbekistan
Ayodeji Olalekan Salau Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Nigeria and Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India
Aitizaz Ali Asia Pacific University of Technology and Innovation, Malaysia https://orcid.org/0000-0002-4853-5093
Tin Tin Ting Faculty of Data Science and Information Technology, INTI International University, Malaysia https://orcid.org/0000-0001-7634-1686

DOI:

https://doi.org/10.47852/bonviewJCCE52025505

Keywords:

cognitive shifts, EEG analysis, resting-state brain activity, neurofeedback games, brain-computer interface (BCI), education reform

Abstract

Brainwave neurofeedback mediated by electroencephalography (EEG) has a high potential in influencing brainwave activity, which is linked to cognitive functions such as attention, stress regulation, and motor learning. Nevertheless, the exact changes in brainwave frequencies, such as those in the sensorimotor regions (C3, C4) during neurofeedback tasks, have not been well addressed. The present research compares EEG brainwave patterns between the resting baseline and the neurofeedback task to clarify the neural dynamics underlying cognitive engagement. Such findings can contribute to developing more efficient neurofeedback protocols for cognitive enhancement and mental health treatments. Twenty healthy individuals (age 18–40 years) with no neurological conditions or prior exposure to neurofeedback were enrolled. EEG was recorded in a 5-minute resting baseline and a 10-minute neurofeedback session aimed at attention, mental workload, and stress regulation. Specifically, the brainwave was decomposed into five frequency bands including Delta (1–4 Hz), Theta (4–8 Hz), Alpha (8–12 Hz), Beta (13–30 Hz), and Gamma (30–50 Hz) and analyzed by the joint application of advanced deep learning algorithms, such as the 1D Convolutional Neural Networks (1D-CNN) and Bidirectional Long Short-Term Memory network (BI-LSTM). These results also underscore the differential role that Alpha, Beta, and Gamma waves play in neurofeedback, supporting improved attention, and cognitive workload regulation, whereas Theta and Delta remained essentially unchanged.

Received: 24 February 2025 | Revised: 27 May 2025 | Accepted: 19 June 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data are available upon reasonable request from the corresponding author.

Author Contribution Statement

Saikat Gochhait: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Project administration. Irina Leonova: Investigation. Prabha Kiran: Resources. Ayodeji Olalekan Salau: Writing - original draft. Aitizaz Ali: Writing - review & editing, Visualization, Supervision. Ting Tin Tin: Writing - review & editing, Visualization, Supervision, Funding acquisition.