Playing table tennis against robotic player makes human brain work harder

To analyze the human player's brain, the team designed a cap with over 100 electrodes mounted on a backpack-sized device. 
Mrigakshi Dixit
A research participant plays against a ball-serving machine while his brain is imaged with an EEG cap.
A research participant plays against a ball-serving machine while his brain is imaged with an EEG cap.

Frazier Springfield 

A one-of-a-kind study documents how a human brain reacts when playing table tennis against a robotic opponent. 

This study by the University of Florida scientists provides intriguing insights into human brain activity. The findings revealed that human players' brains reacted differently to opponents (other humans and ball machines). And playing against a robotic opponent was much more difficult for the human brain.

Scanning the brain activity with electrodes

The team designed a cap with over 100 electrodes mounted on a backpack-sized device to analyze the human player's brain. 

As the electrodes scanned players' brain activity, the team examined the brain area that converts sensory information into movement (a region called parieto-occipital cortex).

“It takes all your senses – visual, vestibular, auditory – and gives information on creating your motor plan. It’s been studied a lot for simple tasks, like reaching and grasping, but all of them are stationary. We wanted to understand how it worked for complex movements like tracking a ball in space and intercepting it and table tennis was perfect for this,” explained Amanda Studnicki, a graduate student at the University of Florida, in a statement.

They gathered data from the game between the ball machine and the human players for dozens of hours in order to obtain definitive results. 

The results showed that their neurons worked in unison when the human players were playing against another human. However, when the player's opponent was a ball-serving machine, the neurons were not synchronized (in neuroscience, this is called desynchronization).

This is simply because the human players' moves were easy to predict, whereas the robotic ball machine did not indicate its next move. As a result, human brains scrambled in this study. 

These findings are significant in sports training because they show that machine players cannot replace human opponents. On the other hand, scientists can continue to work on making robots more naturalistic.

“Robots are getting more ubiquitous. You have companies like Boston Dynamics that build robots that can interact with humans and other companies that are building socially assistive robots that help the elderly. Humans interacting with robots is going to be different than when they interact with other humans. Our long-term goal is to try to understand how the brain reacts to these differences,” said Daniel Ferris, a professor of biomedical engineering at UF. 

The results are published in the journal eNeuro.

Study abstract: 

Traditional human electroencephalography experiments that study visuomotor processing use controlled laboratory conditions with limited ecological validity. In the real world, the brain integrates complex, dynamic, multimodal visuomotor cues to guide the execution of movement. The parietal and occipital cortices are especially important in the online control of goal-directed actions. Table tennis is a whole-body, responsive activity requiring rapid visuomotor integration that presents a myriad of unanswered neurocognitive questions about brain function during real world movement. The aim of this study was to quantify the electrocortical dynamics of the parieto-occipital cortices while playing a sport with high-density electroencephalography. We included analysis of power spectral densities, event-related spectral perturbations, intertrial phase coherences, event-related potentials, and event-related phase coherences of parieto-occipital source-localized clusters while participants played table tennis with a ball machine and a human. We found significant spectral power fluctuations in the parieto-occipital cortices tied to hit events. Ball machine trials exhibited more fluctuations in theta power around hit events, an increase in intertrial phase coherence and deflection in the event-related potential, and higher event-related phase coherence between parieto-occipital clusters as compared to trials with a human. Our results suggest that sport training with a machine elicits fundamentally different brain dynamics than training with a human.

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