A Sub-$\mu$ W Reconfigurable Front-End for Invasive Neural Recording That Exploits the Spectral Characteristics of the Wideband Neural Signal

Valtierra, Jose Luis; Delgado‐Restituto, Manuel; Fiorelli, Rafaella; Rodríguez-Vázquez, Á.

doi:10.1109/tcsi.2020.2968087

Cited by 20 publications

(9 citation statements)

References 47 publications

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“…Other full custom and low-power neural recording headstages developed in the past were optimized for very large scale arrays [20][21][22][23] , or for intracranial recordings 49,[71][72][73][74][75][76] . To our knowledge, we present here the first instance of a headstage design that has the capability of adapting to numerous use cases requiring different gain factors and band selections, on the same input channel.…”

Section: Discussionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

et al. 2021

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The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

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Section: Discussionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

et al. 2021

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“…Although numerous neural recording headstages have been already developed and optimized for their specific application domain (e.g., for very large scale arrays [12][13][14][15] , or for intracranial recordings 38,[52][53][54][55][56][57], to our knowledge, this is the first instance of a headstage design that has the capability of adapting to numerous use cases requiring different gain factors and band selections, on the same input channel.…”

Section: Discussionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Sharifhazileh

Burelo

Sarnthein³

et al. 2020

Preprint

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The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from compact and portable devices that can process these signals locally, in real-time, without the need for off-line processing. An example is the recording of intracranial EEG(iEEG) during epilepsy surgery with the detection of High Frequency Oscillations (HFOs, 80-500 Hz), which are a biomarker for the epileptogenic zone. Conventional approaches of HFO detection involve the offline analysis of prerecorded data, often on bulky computers. However, clinical applications during surgery or in long-term intracranial recordings demand a self-sufficient embedded device that is battery-powered to avoid interfering with other electronic equipment in the operation room. Mixed-signal and analog-digital neuromorphic circuits offer the possibility of building compact, embedded, and low-power neural network processing systems that can analyze data on-line and produce results with short latency in real-time. These characteristics are well suited for clinical applications that involve the processing of biomedical signals at (or very close to) the sensor level. In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting clinically relevant HFOs in iEEG from epilepsy patients. The device was fabricated using a standard 0.18μm CMOS technology node and has a total area of 99 mm2. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3 μW. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity, and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing “neuromorphic intelligence” to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.

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“…At the electrode-AFE interface, three main noise sources can be distinguished: biological or background noise, electrodeelectrolyte interface noise and the noise from the recording electronics (Obien et al, 2015). This is illustrated in Figure 3 (Valtierra et al, 2020).…”

Section: Noise In Electrode-afe Interfacesmentioning

confidence: 99%

“…The background noise, V BN in Figure 3, comprises the electrical activity of other cells surrounding the recording electrode (Obien et al, 2015). This noise source is usually quoted as v n,BN ≈ 10 µV rms although its spectral density distribution is not generally defined (Chandrakumar and Markovic, 2017;Valtierra et al, 2020).…”

Section: Noise In Electrode-afe Interfacesmentioning

confidence: 99%

“…Both flicker and thermal noise contributions will depend on the amplifier topology and operation region. In biomedical applications, amplifiers are commonly biased in sub-threshold region (Muller et al, 2015;Delgado-Restituto et al, 2017;Valtierra et al, 2020). Hence, for an IA employing an operational-transconductance amplifier (OTA), the thermal and flicker noise contributions can be respectively estimated by (Valtierra et al, 2020):…”

Section: Noise In Electrode-afe Interfacesmentioning

confidence: 99%

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Recording Strategies for High Channel Count, Densely Spaced Microelectrode Arrays

Perez-Prieto

Delgado‐Restituto

2021

Front. Neurosci.

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Neuroscience research into how complex brain functions are implemented at an extra-cellular level requires in vivo neural recording interfaces, including microelectrodes and read-out circuitry, with increased observability and spatial resolution. The trend in neural recording interfaces toward employing high-channel-count probes or 2D microelectrodes arrays with densely spaced recording sites for recording large neuronal populations makes it harder to save on resources. The low-noise, low-power requirement specifications of the analog front-end usually requires large silicon occupation, making the problem even more challenging. One common approach to alleviating this consumption area burden relies on time-division multiplexing techniques in which read-out electronics are shared, either partially or totally, between channels while preserving the spatial and temporal resolution of the recordings. In this approach, shared elements have to operate over a shorter time slot per channel and active area is thus traded off against larger operating frequencies and signal bandwidths. As a result, power consumption is only mildly affected, although other performance metrics such as in-band noise or crosstalk may be degraded, particularly if the whole read-out circuit is multiplexed at the analog front-end input. In this article, we review the different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and suggest strategies for improving performance.

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A Sub-$\mu$ W Reconfigurable Front-End for Invasive Neural Recording That Exploits the Spectral Characteristics of the Wideband Neural Signal

Cited by 20 publications

References 47 publications

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Recording Strategies for High Channel Count, Densely Spaced Microelectrode Arrays

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