Human-to-human closed-loop control based on brain-to-brain interface and muscle-to-muscle interface

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Novel communication techniques are of enduring human interest.
• Direct brain-to-brain interfaces (BBI) and muscle-to-muscle interfaces (MMI) represent emerging frontiers in human interaction.
• Integrating artificial data pathways with natural neural pathways offers new possibilities for communication.

Purpose of the Study:

• To present a novel closed-loop system combining brain-to-brain interface (BBI) and muscle-to-muscle interface (MMI).
• To establish functional connections between natural nerve pathways using artificial data flows.
• To evaluate the feasibility and performance of this integrated human interaction mechanism.

Main Methods:

• Utilized electroencephalography (EEG) based brain-computer interface (BCI) to detect sender's intention.
• Employed transcranial magnetic stimulation (TMS) to trigger receiver's hand motion based on sender's brain signals.
• Integrated electromyography (EMG) and functional electrical stimulation (FES) to create a reciprocal feedback loop for hand motion generation.

Main Results:

• Achieved 85% response accuracy in human-controlled loop experiments, demonstrating system feasibility.
• Successfully enabled repetitive and reciprocal hand motion control in automatic control loop tests.
• Subjects accomplished up to 85 consecutive hand motion control cycles during automatic testing.

Conclusions:

• The proposed BBI and MMI combined system is feasible for novel human interaction.
• The closed-loop mechanism effectively translates neural intention into physical action and provides sensory feedback.
• This study paves the way for advanced brain-computer and muscle-computer interfaces.