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

Saikat GochhaitSymbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), India and Samara State Medical University, RussiaIrina LeonovaDepartment of Field and Applied Sociology Vice Dean, Lobachevsky State University of Nizhny Novgorod, RussiaPrabha KiranWestminster International University in Tashkent, UzbekistanAyodeji Olalekan SalauDepartment of Electrical/Electronics and Computer Engineering, Afe Babalola University, Nigeria and Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, IndiaAitizaz AliAsia Pacific University of Technology and Innovation, MalaysiaTing Tin TinFaculty of Data Science and Information Technology, INTI International University, Malaysia
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Аннотация

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 from the corresponding author upon reasonable request. 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. Tin Tin Ting: Writing - review & editing, Visualization, Supervision, Funding acquisition.

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