Periodic Reporting for period 1 - DisPatch EEG (Distributed wireless EEG sensor patches for auditory attention decoding in hearing technology)
Período documentado: 2024-01-01 hasta 2025-06-30
Over 400 million people worldwide suffer from disabling hearing loss. While most can benefit from a hearing aid, many still face serious difficulties in situations where several people are talking at the same time. This often results in social isolation. Algorithms already exist that can separate a single speaker from a mixture of voices. The remaining bottleneck is that a hearing aid does not know which of these speakers the user wants to attend to.
Recent research has shown that auditory attention can be decoded from brain activity using electroencephalography (EEG). However, the integration of this capability into hearing aids is blocked by current EEG hardware limitations. Devices that cover a large area of the scalp are too bulky to wear in daily life. Smaller devices are comfortable but do not record from enough scalp locations.
To address these challenges, we propose a modular system consisting of several wireless EEG sensor patches that can be placed at hairless scalp locations. This wireless EEG sensor network increases scalp coverage and improves decoding accuracy while remaining comfortable and discreet thanks to miniaturized electronics and the absence of connecting wires. Each node consists of a flexible, skin-conforming electrode patch with embedded electronics to collect high-quality EEG recordings and wirelessly share the collected data with other nodes. The system will run energy efficient algorithms that minimize power consumption and extend battery life.
In this project we will develop a fully operational prototype of such a system based on recent advances in hardware design and EEG decoding algorithms.
Recent research has shown that auditory attention can be decoded from brain activity using electroencephalography (EEG). However, the integration of this capability into hearing aids is blocked by current EEG hardware limitations. Devices that cover a large area of the scalp are too bulky to wear in daily life. Smaller devices are comfortable but do not record from enough scalp locations.
To address these challenges, we propose a modular system consisting of several wireless EEG sensor patches that can be placed at hairless scalp locations. This wireless EEG sensor network increases scalp coverage and improves decoding accuracy while remaining comfortable and discreet thanks to miniaturized electronics and the absence of connecting wires. Each node consists of a flexible, skin-conforming electrode patch with embedded electronics to collect high-quality EEG recordings and wirelessly share the collected data with other nodes. The system will run energy efficient algorithms that minimize power consumption and extend battery life.
In this project we will develop a fully operational prototype of such a system based on recent advances in hardware design and EEG decoding algorithms.
We have developed an operational prototype of a modular sensing platform for wearable electroencephalography (EEG) recordings and validated it in several brain–computer interface (BCI) paradigms, including auditory attention decoding. The platform is designed as a wireless EEG sensor network, consisting of multiple miniaturized wireless EEG sensor nodes that synchronously collect data from different scalp locations. With no wires between the sensors, the system allows flexible placement and is discreet in appearance. This stands in contrast to commercial headset-based EEG systems that have fixed electrode configurations and are often bulky. The absence of interconnecting wires also reduces sensitivity to motion artefacts and electromagnetic interference. While commercial systems may offer advantages in certain hardware metrics such as battery capacity or transmission range, our platform is optimized for miniaturization, scalability and modularity.
During the development process we first explored flexible polymer micro-needle electrodes, but these did not provide sufficiently low and stable contact impedance in our validation tests. We therefore redesigned the electrodes using more rigid silicon-based micro-needles with nanostructuring to improve contact impedance.
All components have been integrated into a single sensor node, with a form factor designed for easy attachment and removal. A flexible printed circuit board (flex-PCB) was developed that can be clicked onto adhesive electrodes. The EEG sensor PCB can be plugged into a connector embedded in the flex-PCB. Our system is also compatible with commercial pre-gelled disposable electrodes, enabling quick and efficient short-term setups. The sensor nodes also feature an embedded microphone, which is useful for analyzing neural responses to auditory signals (see below).
The system is described in the following publication:
R. Ding, C. Hovine, P. Callemeyn, M. Kraft and A. Bertrand, "A wireless, scalable and modular EEG sensor network platform for unobtrusive brain recordings", IEEE Sensors Journal, vol. 25, no. 2, 2025, pp. 22580–22590. doi: 10.1109/JSEN.2025.3562791.
The platform was tested in an auditory attention decoding experiment in which participants focused on a target speaker within a two-speaker mixture. Using EEG data collected jointly from multiple miniature sensor nodes, we successfully decoded which speaker the participant was attending to. The results showed that combining multiple sensors significantly improves decoding accuracy compared to a single EEG node. These findings will be presented in a forthcoming publication.
To demonstrate the versatility of the platform, we also tested it in three other brain–computer interface paradigms: steady-state visually evoked potentials, auditory steady state responses, and neural tracking of speech for hearing assessment.
During the development process we first explored flexible polymer micro-needle electrodes, but these did not provide sufficiently low and stable contact impedance in our validation tests. We therefore redesigned the electrodes using more rigid silicon-based micro-needles with nanostructuring to improve contact impedance.
All components have been integrated into a single sensor node, with a form factor designed for easy attachment and removal. A flexible printed circuit board (flex-PCB) was developed that can be clicked onto adhesive electrodes. The EEG sensor PCB can be plugged into a connector embedded in the flex-PCB. Our system is also compatible with commercial pre-gelled disposable electrodes, enabling quick and efficient short-term setups. The sensor nodes also feature an embedded microphone, which is useful for analyzing neural responses to auditory signals (see below).
The system is described in the following publication:
R. Ding, C. Hovine, P. Callemeyn, M. Kraft and A. Bertrand, "A wireless, scalable and modular EEG sensor network platform for unobtrusive brain recordings", IEEE Sensors Journal, vol. 25, no. 2, 2025, pp. 22580–22590. doi: 10.1109/JSEN.2025.3562791.
The platform was tested in an auditory attention decoding experiment in which participants focused on a target speaker within a two-speaker mixture. Using EEG data collected jointly from multiple miniature sensor nodes, we successfully decoded which speaker the participant was attending to. The results showed that combining multiple sensors significantly improves decoding accuracy compared to a single EEG node. These findings will be presented in a forthcoming publication.
To demonstrate the versatility of the platform, we also tested it in three other brain–computer interface paradigms: steady-state visually evoked potentials, auditory steady state responses, and neural tracking of speech for hearing assessment.
The project has delivered two main results. First, we developed a platform of synchronized wireless miniature EEG sensors that allows researchers to easily set up wearable EEG experiments outside of the laboratory. This platform also serves as a proof of concept for the wireless EEG sensor network concept, in which high-density EEG recordings can be collected from concealable sensors placed at different scalp locations. Second, we demonstrated the feasibility of auditory attention decoding using multiple galvanically isolated miniature EEG sensors. By combining novel hardware and software, we showed that this configuration improves the decoding of auditory attention compared to individual sensors.
The EEG sensor network platform offers a unique value proposition through its versatility. It can operate across the full spectrum between maximum spatial electrode coverage and maximum concealability, a flexibility unmatched by current commercial systems. Competing platforms typically cover only a single point in this design space, whereas our solution enables both high-density research-grade recordings and highly concealable wearable setups that are not yet available on the market.
The platform is now close to being ready for distribution to research groups who wish to incorporate it into their experimental designs for auditory research and other applications, as well as to EEG developers seeking a rapid prototyping and testing tool. To progress towards commercialization, several additional steps are required. These include a comprehensive evaluation of regulatory requirements, such as medical device classification, safety standards, and electromagnetic compatibility testing. The design will also need to be refined for manufacturability at scale, with attention to cost optimization, robust quality control procedures, and long-term reliability testing under real-world conditions. Establishing partnerships with manufacturers, securing intellectual property protection, and conducting pilot deployments with early adopters will be important milestones. Finally, a structured plan for customer support, software updates, and maintenance will be essential for sustainable adoption in both research and clinical markets.
The EEG sensor network platform offers a unique value proposition through its versatility. It can operate across the full spectrum between maximum spatial electrode coverage and maximum concealability, a flexibility unmatched by current commercial systems. Competing platforms typically cover only a single point in this design space, whereas our solution enables both high-density research-grade recordings and highly concealable wearable setups that are not yet available on the market.
The platform is now close to being ready for distribution to research groups who wish to incorporate it into their experimental designs for auditory research and other applications, as well as to EEG developers seeking a rapid prototyping and testing tool. To progress towards commercialization, several additional steps are required. These include a comprehensive evaluation of regulatory requirements, such as medical device classification, safety standards, and electromagnetic compatibility testing. The design will also need to be refined for manufacturability at scale, with attention to cost optimization, robust quality control procedures, and long-term reliability testing under real-world conditions. Establishing partnerships with manufacturers, securing intellectual property protection, and conducting pilot deployments with early adopters will be important milestones. Finally, a structured plan for customer support, software updates, and maintenance will be essential for sustainable adoption in both research and clinical markets.