A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC

Shin, Uisub; Ding, Cong; Woods, Virginia; Widge, Alik S.; Shoaran, Mahsa

doi:10.1109/lssc.2023.3238797

Cited by 9 publications

(14 citation statements)

References 13 publications

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“…The sine and cosine are typically extracted by computing the phase from the complex signals and obtaining the sine and cosine values of that phase. This two-step extraction process can be achieved using CORDIC processors [9][10][11] or a light phase extractor (LPE) associated with a trigonometric lookup table (LUT) [14][15][16]. Magnitude extraction, on the other hand, is performed using a CORDIC processor [9][10][11] or the L-infinity norm approximation [14][15][16].…”

Section: Plv/pac Computation Techniquesmentioning

confidence: 99%

“…This two-step extraction process can be achieved using CORDIC processors [9][10][11] or a light phase extractor (LPE) associated with a trigonometric lookup table (LUT) [14][15][16]. Magnitude extraction, on the other hand, is performed using a CORDIC processor [9][10][11] or the L-infinity norm approximation [14][15][16]. This section explains the CORDIC processors algorithm, the LPE with LUT technique, and the l-infinity norm.…”

Section: Plv/pac Computation Techniquesmentioning

confidence: 99%

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PLV/PAC Feature Extraction Units for Implantable Neural Interfaces: Review

Hasaneen,

Yassin

2024

Aswan University Journal of Sciences and Technology

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Neural interfaces shows promise in treating neurological conditions such as Epilepsy, Depression, and Parkinson's disease. To enable fully implantable treating interfaces, efficient oscillatory feature extraction units are required. This article explores different techniques suggested for extracting phase locking value (PLV) and phase amplitude coupling (PAC) features. Additionally, the article provides an overview of the current state-of-the-art units and highlights their limitations.

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Section: Plv/pac Computation Techniquesmentioning

confidence: 99%

Section: Plv/pac Computation Techniquesmentioning

confidence: 99%

PLV/PAC Feature Extraction Units for Implantable Neural Interfaces: Review

Hasaneen,

Yassin

2024

Aswan University Journal of Sciences and Technology

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“…In order to extend the application of this system to a wide range of brain disorders, a rich feature-extraction engine subsequently computes relevant biomarkers in the spectral, temporal, as well as phase domains (the latter is particularly useful for network-based diseases such as psychiatric and memory disorders). 79,80 To enable on-site, real-time detection of the seizure state or other neurological conditions, this system embeds an accurate multi-class probabilistic NeuralTree algorithm with two-layer neural networks implemented in the internal nodes. 81 Network pruning and weight quantization were used to compress the networks, requiring only 2.93 kB of on-chip memory.…”

Section: Intelligent Neural Prosthesesmentioning

confidence: 99%

“…81 Network pruning and weight quantization were used to compress the networks, requiring only 2.93 kB of on-chip memory. Circuit-algorithm co-design techniques such as energy-aware regularization 80 enabled an ultra-low-energy classifier, leveraging high-density (256-Ch.) training and channel-selective (64-Ch.)…”

Section: Intelligent Neural Prosthesesmentioning

confidence: 99%

“…This microchip integrates four modules of 64‐channel low‐noise time‐division multiplexed (TDM) mixed‐signal front‐ends for high‐spatial‐resolution neural recording. In order to extend the application of this system to a wide range of brain disorders, a rich feature‐extraction engine subsequently computes relevant biomarkers in the spectral, temporal, as well as phase domains (the latter is particularly useful for network‐based diseases such as psychiatric and memory disorders) 79,80 …”

Section: Current and Future Devicesmentioning

confidence: 99%

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From basic sciences and engineering to epileptology: A translational approach

et al. 2023

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Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal‐processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)–enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data‐sharing initiatives.

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Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Liu,

Younk,

M Drahos

et al. 2024

J. Neural Eng.

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Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring <310 ms for training, <0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

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A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC

Cited by 9 publications

References 13 publications

PLV/PAC Feature Extraction Units for Implantable Neural Interfaces: Review

PLV/PAC Feature Extraction Units for Implantable Neural Interfaces: Review

From basic sciences and engineering to epileptology: A translational approach

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

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