Hybrid Brain Interface for Robotic Swarm Control
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Solution Overview
Problem
Existing brain-computer interface (BCI) systems face limitations in controlling multiple degrees of freedom due to binary control and lengthy training sessions, with hybrid systems experiencing complexity and cross-talk issues when combining EEG modalities.
Innovation Solution
A hybrid BCI system combining EEG signals with joystick input, utilizing ERD/ERS phenomena, Principal Component Analysis (PCA), and Hidden Markov Models (HMMs) to enable more dexterous control strategies for robotic systems, requiring minimal training and robust across multiple subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EEG signals are used to control robotic systems, then the number of degrees of freedom controlled is limited (binary control), but the system requires minimal training
Solution Approach 1:
The patent combines EEG signals with joystick input to create a hybrid control system. The EEG component handles high-level planning and discrete decisions, while the joystick provides continuous control for positioning and manipulation. This merging allows the system to control multiple degrees of freedom simultaneously without requiring extensive training sessions, as the joystick provides intuitive control while EEG adds cognitive control capabilities.
2Measurement precision
If multiple EEG modalities are combined in hybrid BCI systems, then the accuracy of brain state classification is enhanced, but the device complexity increases due to interface switching and cross-talk
Solution Approach 1:
The patent introduces a joystick as an intermediary device that mediates between the user's cognitive intent (detected via EEG) and the robotic system control. Rather than combining multiple complex EEG modalities that require switching and synchronization, the system uses a single EEG modality for discrete high-level commands and the joystick for continuous control, simplifying the interface while maintaining enhanced accuracy.
3Reliability
If discrete brain states are used for robotic control, then the system is robust and customizable without lengthy training, but the number of degrees of freedom is restricted
Solution Approach 1:
The system merges discrete EEG-based brain state control with continuous joystick control. The EEG component provides robust discrete high-level planning commands that are reliable and require minimal training, while the joystick component adds continuous control capabilities for positioning and manipulation, effectively increasing the degrees of freedom without compromising the robustness of the brain state classification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system allows for intuitive, real-time control of robots with increased degrees of freedom, achieving high accuracy and completion rates with minimal training, as demonstrated by controlling a swarm of quadrotors and virtual interfaces.
Implementation Method 1
Most of them rely on the analysis of ElectroEncephaloGraphic (EEG) signals and their features
Implementation Method 2
The system exploits the ERD/ERS phenomena that takes place during limb movement imagination
Data Source
AI summary
A system and a method for hybrid brain computer interface for controlling a robotic swarm are disclosed. The system comprises: a control interface for defining a plurality of electrodes configured for accessing a plurality of brain signals associated with a plurality of channels located over a sensorimotor cortex; a processor, in operative communication with the control interface, configured to: train a machine learning model to associate a brain state with data defining a brain signal, decode a control signal of the plurality of brain signals to generate control data, apply the control data to the machine learning model to identify the brain state, and transmit instructions to a plurality of robotic devices to modify a density of the plurality of robotic devices based on the brain state; and an input device, in operable communication with the processor, for generating input data, the input data utilized by the processor to modify a position of the plurality of robotic devices.