Summary:
UCLA researchers in the Department of Electrical and Computer Engineering have developed compact, low-power neural signal sensing solutions to enhance human-machine interfaces (HMI), including AR/VR systems.
Background:
Electrogram technologies, such as electroencephalogram (EEG) and electromyography (EMG), are widely used to capture biological signals, such as electrical potentials, produced by the human body. These signals are acquired using specialized apparatus, such as hats or gloves embedded with electrodes, and processed to assess key physiological functions and support disease diagnosis. Recently, there has been a growing interest in extending these technologies beyond clinical settings, driving the development of next generation human-machine interface (HMI) devices. These HMI devices aim to interpret user intent solely from EEG or EMG devices, enabling control through hand gestures or even completely hands-free operation. Emerging applications include interacting with environments in VR/AR systems, as well as enabling direct communication for patients with speech impairments. However, current EEG and EMG systems require bulky setups involving multiple electrode sensors, wired connections, and dedicated processing units. These systems also demand significant power and processing bandwidth, limiting their portability and comfort, and making them unsuitable for latency-sensitive applications such as gaming or real-time communication. There is an unmet need for lightweight, energy-efficient sensing solutions to enable next-generation EEG and EMG systems for HMI applications. "
Innovation:
Dr. Babakhani and colleagues have developed two compact, low-power neural sensing technologies tailored for next generation HMI devices. The first is an EMG-based solution, which utilizes multiple wirelessly-powered, portable EMG sensors to simultaneously record biological signals from various locations across the human body. The second is a radar-based solution featuring a wearable ring-shaped, low-power radar sensor capable of detecting subtle finger movements as small as 100μm. Both technologies support seamless wireless data transmission via Wi-Fi or Bluetooth connections to commercial AR/VR glasses.
By significantly reducing the device footprint and energy consumption, these innovations eliminate the need for bulky sensor arrays and expensive processing units, presenting a significant improvement in this field. The proposed technologies are compatible with a wide range of consumer electronics, such as smartphones, AR/VR glasses, and other wearable devices. This technology has the potential to enable a broad range of applications across healthcare, entertainment, productivity tools, and other such areas that rely on gesture-based or hands-free control for next generation HMI systems.
Potential Applications:
• Gesture-based and hands-free control on AR/VR platform
• Assistive communication and mobility devices for individuals with motor or speech impairments
• Wearable health and fitness devices
• Immersive videogame and entertainment applications
• Safety tracking for fleet transportation (trucking, aviation, defense)
• Prosthetic development with neural or motor control
• Surgical navigation and control tools
Advantages:
• Compact and lightweight design
• Ultra-low-power consumption
• Wireless-powered operation
• High sensitivity to fine movements
• Wireless data transmission via Wi-Fi or Bluetooth to commercial AR/VR devices
State of Development:
Successful ex vivo validation completed.
Related Papers:
1. R. P. Mathews, H. Jafari Sharemi, I. Habibagahi, J. Jang, A. Ray and A. Babakhani, "Towards a Miniaturized, Low Power, Batteryless, and Wireless Bio-Potential Sensing Node," 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), Taipei, Taiwan, 2022, pp. 404-408, doi: 10.1109/BioCAS54905.2022.9948685.
2. Lyu, Hongming, and Aydin Babakhani. "Systems and Methods for Long-Distance Remote Sensing With Sub-Wavelength Resolution Using a Wirelessly-Powered Sensor Tag Array." U.S. Patent Application 17/597,461, filed August 18, 2022.
3. Razavian, Sam, Sidharth Thomas, Mostafa Hosseini, and Aydin Babakhani. "Micro-Doppler Detection and Vibration Sensing Using Silicon-Based THz Radiators." IEEE Sensors Journal 22, no. 14 (2022): 14091-14101.
Reference:
UCLA CASE No. 2023-146-1
Lead Inventor:
Aydin Babakhani, Department of Electrical and Computer Engineering