CMOS-compatible purely capacitive interfaces for high-density in-vivo recording from neural tissue

Schröder, Sven; Cecchetto, Cinzia; Keil, Stefan; Mahmud, Mufti; Brose, Evelin; Doǧan, Oğuz; Bertotti, Gabriel; Wolanski, Dirk; Tillack, Bernd; Schneidewind, Jessica; Gargouri, Hassan; Arens, Michael; Bruns, J.; Szyszka, Bernd; Vassanelli, Stefano; Thewes, R.

doi:10.1109/biocas.2015.7348358

Cited by 23 publications

(19 citation statements)

References 17 publications

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“…The evoked LFPs are recorded from the barrel cortex of a single Wistar rat at the University of Padova. The recordings are performed at 15 µm resolution with a 16×16 CMOSbased sensor array [7] shown in Fig. 1.…”

Section: Data Setmentioning

confidence: 99%

“…However, with recent technological developments, highdensity multi-electrode arrays (MEAs) based on CMOS process became the state-of-the-art [6]. They were introduced for large-scale high-resolution in vitro and, only very recently, in vivo applications [7]- [10]. The data acquired by these miniaturized electrode matrices are meaningless if not further processed and analyzed to extract * Corresponding author: xiaywang@iis.ee.ethz.ch actionable information.…”

Section: Introductionmentioning

confidence: 99%

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Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Wang

Magno

Cavigelli

et al. 2018

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16×16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6 mW.

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Section: Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Wang

Magno

Cavigelli

et al. 2018

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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“…Thus, the study of these bio-signals gives the clue of how the brain represents the somatosensory stimuli. [19].…”

Section: Background and System Overviewmentioning

confidence: 99%

Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor

Wang

Magno

Cavigelli

et al. 2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16×16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward realtime decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.

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“…In spite of the strict power and size constraint for in-vivo silicon probes, new prototypes with high-density contacts are being developed and tested in-vivo. In Schröder et al [1], a MEA with 256 recording sites (a 16×16 array) was used to monitor the functional electrical imaging [2] of event-related Local Field Potentials (LFPs) in the rat's barrel cortex. Such high spatial information density can be exploited to refine localization techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Our computation is based on the 2D electrode distribution of the MEA prototype in [1]. A Convolutional Neural Network with a machine learning algorithm exploits the rich spatial information by this geometry for improved prediction.…”

Section: Introductionmentioning

confidence: 99%

Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning

Buccino

Ness

Einevoll

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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With the latest development in the design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, the spatiotemporal information in the recorded signals allows for refined estimation of a neuron's location around the probe. In parallel, advances in computational models for neural activity enables simulation of recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on a large set of such simulation data to extract the 3D position of the neuronal somata. Multi-compartment models from 13 different neural morphologies in layer 5 (L5) of the rat's neocortex are placed at random locations and with different alignments with respect to the MEA. The sodium trough and repolarisation peak images on the MEA serve as input features for a Convolutional Neural Network (CNN), which predicts the neural location robustly and with low error rates. The forward modeling/machine learning approach yields very accurate results for the different morphologies and is able to cope with different neuron alignments.

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CMOS-compatible purely capacitive interfaces for high-density in-vivo recording from neural tissue

Cited by 23 publications

References 17 publications

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array

Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor

Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning

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