Researchers at the Indian Institute of Technology (IIT), Kanpur are delving into the activity of alpha waves in the brain, typically associated with wakeful relaxation, to decipher how stress influences crucial cognitive functions such as attention, working memory, and the analysis of risk versus reward.
Information was available with The Chenab Times that the essence of this study is to understand the diverse ways individuals react to stress and how this stress can modulate human cognition. The research team intends to employ non-invasive techniques like electroencephalogram (EEG) to develop automated models for various stress dimensions. These models will correlate stress with factors such as the feeling of lost control, helplessness, and anxiety, as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5).
Alpha waves, first identified approximately a century ago, are known to be prevalent when a person is in a relaxed, wakeful state, often indicative of a calm, meditative mind, particularly with closed eyes. Previous studies, including one published in the Institute of Electrical and Electronics Engineers (IEEE) Xplore in October 2025, have explored stress mitigation by exposing individuals to binaural beats within the alpha frequency range of 8-12 Hertz. This involved presenting slightly different frequencies to each ear simultaneously, an auditory illusion that prompts the brain to create a third, distinct frequency. The findings from such experiments indicated a notable increase in alpha wave activity, which corresponded with participants reporting reduced levels of perceived stress.
The IIT-Kanpur contingent, under the leadership of Tushar Sandhan, an associate professor of electrical engineering, is specifically examining alpha wave activity within the frontal lobe region of the brain using EEG. This area is paramount for functions including judgment, self-perception, and decision-making. The research also encompasses the study of ‘frontal alpha symmetry,’ a biomarker that signifies an imbalance where one hemisphere of the brain exhibits more alpha wave activity than the other. Such asymmetry has been observed to be pronounced in certain psychiatric and neurological conditions, notably depression.
Sandhan elaborated that frontal alpha asymmetry has been a subject of extensive research, particularly within affective neuroscience and depression studies. He noted that numerous prior investigations have linked depression to greater left frontal alpha power, which can be interpreted as reduced left frontal activity, subsequently associated with diminished approach motivation. Approach and withdrawal motivations are behavioural responses that drive individuals either towards a reward or away from a perceived threat.
The research apparatus includes a custom-assembled bioamplifier fitted with soft, flexible silicon electrodes for the EEG, integrated into a specially designed 3D-printed ergonomic headband. The collection of cardiac activity is also being facilitated through a smartwatch, providing a multi-modal approach to data acquisition. This comprehensive data collection aims to capture a more holistic picture of physiological and cognitive responses to stress.
In prior work, Sandhan and his collaborators developed ‘DAAFNet,’ an algorithm designed to analyse EEG data for the identification and classification of human emotions. Affective computing, an interdisciplinary field encompassing artificial intelligence, psychology, and cognitive science, focuses on creating systems capable of recognising and interpreting human emotions. The systems developed through affective computing have significant applications in human-computer and brain-computer interfaces, serving to bridge the gap between human intent and machine action, with potential uses ranging from consumer electronics to medical rehabilitation.
Despite the extensive research on alpha waves over the past century, an expert pointed out that data remains insufficient regarding their precise nature as a biomarker. Further longitudinal studies are deemed necessary to establish a more definitive understanding. Vaibhav Tripathi, an assistant professor in the cognitive and brain sciences department at IIT Gandhinagar, highlighted that while alpha waves are prominent and clearly visible on an EEG when eyes are closed, the challenge lies in their association with various cognitive functions and physiological responses like stress. The intensity and pattern of these oscillations can differ significantly among individuals due to variations in their mental states and inherent traits.
Tripathi further explained that an individual’s mental state is dynamic, fluctuating throughout the day and across different days. Factors such as energy levels, engagement in tasks, emotional experiences, or mood shifts can influence these states. His own laboratory is investigating alpha-wave signatures in individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder, among other research areas. He also touched upon the distinction between trait-level differences, referring to an individual’s general predisposition towards happiness or a tendency towards depression or stress, and state-level changes.
“Whether alpha waves or alpha rhythm are associated with some trait property or some state property, it’s still a matter of debate as to the nature of information that alpha waves provide as a biomarker,” Tripathi stated. He underscored the transient and variable nature of stress, suggesting that an objective measure of stress would be beneficial for research. “So, that’s why these studies are challenging. We need an objective measure of stress so that it can account for aspects, such as state-level differences in alpha waves across a day and across multiple days,” he concluded, emphasizing the need for robust methodologies to capture the complex interplay between stress and cognitive function.
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