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Home / Education / Brainwaves study can tell if a worker can handle crisis situation, finds IIT Madras research

Brainwaves study can tell if a worker can handle crisis situation, finds IIT Madras research

According to a study published in the Journal of Computers and Computer Engineering, the researchers found that EEG, a technique that measures brain activity, can measure the cognitive workload of human operators in a chemical plant control room.

education Updated: Sep 01, 2020 15:28 IST
Press Trust of India| Posted by Nandini
Press Trust of India| Posted by Nandini
Madras
Representative
Representative(Getty Images/iStockphoto)

Researchers at Indian Institute of Technology (IIT) Madras have found a method to determine whether a worker has the mental capacity to handle a crisis in a factory or other high-stress jobs by measuring their brainwaves using an electroencephalogram (EEG).

According to a study published in the Journal of Computers and Computer Engineering, the researchers found that EEG, a technique that measures brain activity, can measure the cognitive workload of human operators in a chemical plant control room.

EEG involves placing sensors on the scalp of the subject and measuring brainwave activity. The IIT research has found that measuring brainwaves can help in assessing the capability of an individual to respond to an emergency in real-time, which, in turn, could prevent accidents and mishaps.

According to the team, the research has shown the potential of EEG to assess the cognitive workload of human operators in a chemical plant control room. The cognitive workload is the level of measurable mental effort that is expended by an individual to perform a task. High cognitive workload state of workers makes them prone to commit errors that can lead to accidents.

“Human errors are the cause of nearly 70 per cent of industrial accidents, the world over. Human errors, whether at the planning or execution stage, depend not only on the skill of the worker but also on his or her mental state and sharpness at that time. Anybody’s performance will become error-prone if there is a mismatch between the demands of the task which the person is responsible and their ability at that moment to handle it,” said Rajagopalan Srinivasan, professor at Department of Chemical Engineering, IIT Madras.

“Such a mismatch leads to high cognitive workload in human operators, often a precursor to poor performance. All our thoughts and activities are driven by electrical signals between the cells in our brain called brainwaves, which occur at different frequencies and are called alpha, beta, gamma, theta and delta. The relative magnitudes of these waves along with their variation are a signature of our thought process and current mental state,” he added.

The research team affixed sensors to the heads of six participants and had them perform eight tasks each.

“The nature of the tasks was to monitor a typical industrial section for any disturbances which if not controlled, by the participant, in a given time frame can lead to accidents. Thus, the nature of the job required them to understand the plant (industrial section) behaviour and take appropriate decisions and actions if any disturbance occurred. The disturbance increased their cognitive workload, and only if the correct decision was made, did the cognitive workload reduce.

“Their results showed that the amount of Theta Waves could identify any mismatch between the worker’s mental model of the process and the actual plant behaviour during abnormal situations. This makes sense because the ‘theta band’ of brainwaves has been thought to be responsible for the control process of working memory functions,” he said.

The institute plans to study potential of these EEG methods to improve human performance in various high-risk industries, thus opening a new paradigm to industrial safety and its relation to the real-time mental state of the worker.

“The EEG based approach can provide information about the cognitive workload of operators during training, which in turn can be used to fine-tune the training process itself. It can also provide targeted cues during learning, to improve the overall effectiveness of training,” Srinivasan said.

ht epaper

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