Human cooperation relies on social interaction to achieve a common goal, and the effect of human-computer interaction may also be affected and enhanced by various social interaction factors. The latest research induces brain-brain coupling modulation between subjects through social interaction, which significantly improves the performance of brain-computer interface systems. The results were completed by a research team from the Laboratory of Intelligence and Biomechanics at Tsinghua University and published in the journal Cyborg and Bionic Systems.

Background:

Brain-computer interface (BCI) systems connect neural activity with external devices, bypassing traditional neuromuscular pathways, freeing humans from the limitations of physical limbs and providing a promising approach to motor augmentation. However, there is a large variability in BCI decoding accuracy among different individuals, which limits its further application.

Studies have shown that the perception of multi-layered neural information obtained from tactile and vestibular and visual feedback can significantly affect the performance of BCI systems. Therefore, the role of multisensory perception in the process of social interaction may be similar, which has positive significance for the performance improvement of BCI system.

The purpose of this study was to explore the brain-brain coupling phenomenon in two-person cooperative BCI training, and through the analysis of hyperscan data of EEG, it was found that social interaction can modulate the event-related desynchronization (ERD) and brain functional connectivity of subjects, and significantly improve the classification performance of the BCI system.

Overview of the study

Overview of experimental setup and ultrascan data analysis

A total of 12 pairs of friends were recruited as the experimental group and 10 pairs of strangers were recruited as the control group. One person from each pair of subjects is assigned as a leader and the other as a follower (the leader role holds the middle grip if hand contact is required). The two were asked to perform motor imagery tasks under 4 different conditions: no social interaction, eye interaction, touch interaction, and eye-touch interaction. Subjects were asked to imagine moving the robot from one preset side to the other with their right hand. In this study, 64-lead EEG ultrascan data were collected, and cortical activation, intercerebral functional connectivity, and BCI system performance were analyzed and evaluated.

Overview of experimental design

Cortical activation

The researchers compared the cortical activation modulation between the experimental group and the control group under different interaction conditions, and the results showed that the cortical activation of the contralateral sensorimotor cortex in the experimental group was significantly enhanced by touch interaction and eye-touch interaction, while there was no significant difference between the control group.

Comparison of cortical activation

Brain-coupling networks

The researchers further explored the pattern of intercerebral coupling networks between leaders and followers in the experimental group. Under the condition of eye interaction, the theta-band interbrain coupling network mainly involves the frontal, parietal, and occipital regions of the leader, and the frontal center and parietal lobe regions of the follower. Under the condition of touch interaction, the alpha-band interbrain coupling network mainly involves the parieto-occipital and frontal central regions of the leader, and the frontal and parietal regions of the followers.

Brain-coupling network patterns

BCI system performance improvements

Compared with the non-social interaction condition, the decoding performance of the BCI system of the eye interaction condition and the eye-touch interaction condition in the experimental group was significantly improved, but there was no significant difference in the touch interaction condition. In contrast, there was no significant difference in the decoding performance of the BCI system in the control group.

Comparison of BCI system decoding performance

Research implications

This paper investigated the effects of social interaction on neural oscillations and interbrain coupling during motor imagery-based BCI training. Studies have found that multimodal sensory feedback can enhance cortical activation and promote the coupling of brain regions. The authors note that the introduction of social interaction in BCI-based robot control can further improve the performance of BCI while enhancing the neural synchronization of subjects with each other.

Content Sources:

Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling | Cyborg and Bionic Systems 2024

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