Periodic Reporting for period 3 - IoN (Intranet of Neurons: A Minimally-invasive and High-capacity Transcranial Telemetry Network for Large-scale Brain-wide Neural Recordings)
Reporting period: 2023-06-01 to 2024-05-31
1. Problems being addressed:
- Current intracortical brain-computer interfaces (iBCI) are severely limited by the lack of a high-bandwidth and miniature wireless telemetry system to support the recording from a large number of neurons with high spatial and temporal resolution. Progress in comprehending the brain and nervous system largely relies on the capacity to simultaneously record and transmit extracellular electrical activity from a vast number of neurons. Studies indicate that the number of neurons being concurrently recorded has doubled every 6.4 years since the 1960s. However, existing wireless telemetry systems fall short in efficiently transmitting neural data from even only 100 recording channels, due to the substantial size of extracellular neural data or "spikes,". This limitation hampers real-time analysis and interpretation of neural signals in various research settings. Consequently, this leads to increased energy consumption for wireless telemetry and data routing, posing risks such as tissue damage, hindrance in miniaturization, and reliance on large rechargeable batteries, which can result in chemical leakage or overheating.
- Existing transcranial telemetry systems of iBCIs have limitations that prevent scaling up to a network for brain-wide spatial coverage. In order to increase spatial sampling and to understand the spatial interactions, several neuroscience groups presented a highly distributed neural recording in multiple regions of the cortex. However, existing wireless telemetry systems for iBCI are either too bulky or lack of a efficient network method that allows distributed brain implants in the iBCI to communicate with each other.
2. Society importance: The research and technologies advances in this project will lead to significant impacts in a range of application areas, in particular iBCI and low-latency closed-loop neuromodulation:
- Implantable iBCIs: Several neurotechnology companies, e.g. Neuralink, etc., aim to develop iBCIs for various therapeutic purposes. A key limitation in their BCI prototypes is the absence of high-bandwidth wireless telemetry, essential for diagnostic purposes. The proposed retinomorphic encoding method reduces data size and latency by 10-100×, significantly reducing the technical barrier of employing wireless telemetry. This can enable the full diagnosis capability, which will be crucial for many scenarios.
- Low-latency closed-loop neuromodulation (CLN): Closed-loop neuromodulation uses real-time feedback to adjust the parameters (e.g. timing, intensity, etc) of the neural stimulation delivered to the brain or nerves, to optimize the effectiveness of stimulation in response to the ongoing brain's (or nerve’s) activities. The neural signal propagation between different parts of the nervous systems (e.g. brain to spinal cord) happens between 1’s and 10’s of milliseconds [5]. However, routing and processing large data recorded from high-channel count neural sensors inevitably require data buffering and pooling which introduce latency (>100’s ms) longer than the processing time of nervous tissues. The neural data compression and distributed neural telemetry reduce data size by more than an order and avoid data buffering, so latency can be significantly reduced to sub-millisecond. Low latency in CLN provides more precise and accurate control over the neural stimulation, reducing the risk of side effects and complications. One example is implantable seizure monitoring and intervention devices for patients with epilepsy. This innovation would allow for real-time monitoring and rapid detection of seizure events, enabling timely intervention to mitigate the impact of seizures and improve patient outcomes.
3. Research objectives
There are three primary objectives in this project:
- Neural implant compressive telemetry (WP1): we will develop new loss-less or low-loss data compression and telemetry methods, which use minimum hardware resource (power consumption, area) while achieving >10x data compression and transfer.
- Free-floating implant telemetry (WP2): we will explore new wireless data and power transfer modalities such that we can avoid to use bulky antenna and coils as adopted in most of conventional neural implant telemetry systems, allowing the implants being free-floating and placed on the surface of the cortex to minimize brain tissue scaring.
- Distributed neural telemetry (WP3): we will research efficient telemetry network to allow efficient data transfer between multiple spatially distributed neural implants, with low energy consumption and low latency.
- Current intracortical brain-computer interfaces (iBCI) are severely limited by the lack of a high-bandwidth and miniature wireless telemetry system to support the recording from a large number of neurons with high spatial and temporal resolution. Progress in comprehending the brain and nervous system largely relies on the capacity to simultaneously record and transmit extracellular electrical activity from a vast number of neurons. Studies indicate that the number of neurons being concurrently recorded has doubled every 6.4 years since the 1960s. However, existing wireless telemetry systems fall short in efficiently transmitting neural data from even only 100 recording channels, due to the substantial size of extracellular neural data or "spikes,". This limitation hampers real-time analysis and interpretation of neural signals in various research settings. Consequently, this leads to increased energy consumption for wireless telemetry and data routing, posing risks such as tissue damage, hindrance in miniaturization, and reliance on large rechargeable batteries, which can result in chemical leakage or overheating.
- Existing transcranial telemetry systems of iBCIs have limitations that prevent scaling up to a network for brain-wide spatial coverage. In order to increase spatial sampling and to understand the spatial interactions, several neuroscience groups presented a highly distributed neural recording in multiple regions of the cortex. However, existing wireless telemetry systems for iBCI are either too bulky or lack of a efficient network method that allows distributed brain implants in the iBCI to communicate with each other.
2. Society importance: The research and technologies advances in this project will lead to significant impacts in a range of application areas, in particular iBCI and low-latency closed-loop neuromodulation:
- Implantable iBCIs: Several neurotechnology companies, e.g. Neuralink, etc., aim to develop iBCIs for various therapeutic purposes. A key limitation in their BCI prototypes is the absence of high-bandwidth wireless telemetry, essential for diagnostic purposes. The proposed retinomorphic encoding method reduces data size and latency by 10-100×, significantly reducing the technical barrier of employing wireless telemetry. This can enable the full diagnosis capability, which will be crucial for many scenarios.
- Low-latency closed-loop neuromodulation (CLN): Closed-loop neuromodulation uses real-time feedback to adjust the parameters (e.g. timing, intensity, etc) of the neural stimulation delivered to the brain or nerves, to optimize the effectiveness of stimulation in response to the ongoing brain's (or nerve’s) activities. The neural signal propagation between different parts of the nervous systems (e.g. brain to spinal cord) happens between 1’s and 10’s of milliseconds [5]. However, routing and processing large data recorded from high-channel count neural sensors inevitably require data buffering and pooling which introduce latency (>100’s ms) longer than the processing time of nervous tissues. The neural data compression and distributed neural telemetry reduce data size by more than an order and avoid data buffering, so latency can be significantly reduced to sub-millisecond. Low latency in CLN provides more precise and accurate control over the neural stimulation, reducing the risk of side effects and complications. One example is implantable seizure monitoring and intervention devices for patients with epilepsy. This innovation would allow for real-time monitoring and rapid detection of seizure events, enabling timely intervention to mitigate the impact of seizures and improve patient outcomes.
3. Research objectives
There are three primary objectives in this project:
- Neural implant compressive telemetry (WP1): we will develop new loss-less or low-loss data compression and telemetry methods, which use minimum hardware resource (power consumption, area) while achieving >10x data compression and transfer.
- Free-floating implant telemetry (WP2): we will explore new wireless data and power transfer modalities such that we can avoid to use bulky antenna and coils as adopted in most of conventional neural implant telemetry systems, allowing the implants being free-floating and placed on the surface of the cortex to minimize brain tissue scaring.
- Distributed neural telemetry (WP3): we will research efficient telemetry network to allow efficient data transfer between multiple spatially distributed neural implants, with low energy consumption and low latency.
1. Neural implant compressive telemetry:
- Even-based neuromorphic neural sensing and feature extraction ASIC (presented in IEEE JSSC 2022) : This work presents a bio-inspired event-driven neuromorphic sensing system (NSS) capable of performing on-chip feature extraction and “send-on-delta” pulse-based transmission, targeting implantable neural recording applications.
- Event-based Neural Compressive Telemetry with Loss-less Data Reduction ASIC (presented in IEEE TBioCAS 2024): IoN introduces an event-based neural compressive telemetry (NCT) consisting of channel-rotating ∆-ADCs, an event-driven serializer supporting a novel "ternary address event representation protocol", and an event-based LVDS driver.
2. Miniaturization of the implant telemetry:
- Galvanic-coupled Trans-dural Data Transfer for High-bandwidth Intra-cortical Neural Sensing (presented in IEEE TMTT 2022)
- Ultrasound trans-dural power transfer (presented in IEEE ISSCC 2024): A pitch-matched ultrasound powering TX ASIC for implantable brain-computer interfaces is designed and implemented in IoN.
3. Distributed neural telemetry
- High data rate IR-UWB wireless transmitter ASIC (presented in IEEE JSSC 2022): This work presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces.
- An energy efficient and stable clock generation (presented in IEEE SSCL 2024): This work presents an on-chip clock featuring a embedded temperature sensor.
- Spatially Diverse Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces (presented in IEEE TBioCAS 2024): A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is demonstrated in IoN.
- Even-based neuromorphic neural sensing and feature extraction ASIC (presented in IEEE JSSC 2022) : This work presents a bio-inspired event-driven neuromorphic sensing system (NSS) capable of performing on-chip feature extraction and “send-on-delta” pulse-based transmission, targeting implantable neural recording applications.
- Event-based Neural Compressive Telemetry with Loss-less Data Reduction ASIC (presented in IEEE TBioCAS 2024): IoN introduces an event-based neural compressive telemetry (NCT) consisting of channel-rotating ∆-ADCs, an event-driven serializer supporting a novel "ternary address event representation protocol", and an event-based LVDS driver.
2. Miniaturization of the implant telemetry:
- Galvanic-coupled Trans-dural Data Transfer for High-bandwidth Intra-cortical Neural Sensing (presented in IEEE TMTT 2022)
- Ultrasound trans-dural power transfer (presented in IEEE ISSCC 2024): A pitch-matched ultrasound powering TX ASIC for implantable brain-computer interfaces is designed and implemented in IoN.
3. Distributed neural telemetry
- High data rate IR-UWB wireless transmitter ASIC (presented in IEEE JSSC 2022): This work presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces.
- An energy efficient and stable clock generation (presented in IEEE SSCL 2024): This work presents an on-chip clock featuring a embedded temperature sensor.
- Spatially Diverse Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces (presented in IEEE TBioCAS 2024): A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is demonstrated in IoN.
The expected results till the end of the IoN project in Q3 2026
- The IoN project will demonstrate miniature implantable sub-dural device (below the dural) and cranial device (above the dura and in the skull), which employs galvanic coupling as uplink data communication and ultrasound powering. This will demonstrate the feasibility of allowing brain implant being free-floating, minimizing the one of the most crucial problem of long-term brain implant.
- The IoN project will develop a retina-inspired distributed telemetry method that achieve the best trade-off between communication energy and computing resource for the implant. IoN will demonstrate a implantable wireless network that can support simultaneously wireless telemetry of more than 1000's of recording channels. This will solve one of the biggest issues in data readout bottleneck of high-bandwidth BCI.
- The IoN project will demonstrate miniature implantable sub-dural device (below the dural) and cranial device (above the dura and in the skull), which employs galvanic coupling as uplink data communication and ultrasound powering. This will demonstrate the feasibility of allowing brain implant being free-floating, minimizing the one of the most crucial problem of long-term brain implant.
- The IoN project will develop a retina-inspired distributed telemetry method that achieve the best trade-off between communication energy and computing resource for the implant. IoN will demonstrate a implantable wireless network that can support simultaneously wireless telemetry of more than 1000's of recording channels. This will solve one of the biggest issues in data readout bottleneck of high-bandwidth BCI.