A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

Seong, Changyu; Lee, Wonjae; Jeon, D.

doi:10.1109/tbcas.2021.3134660

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…A classic approach to solve a classification/clustering problem is to allocate each data point to the neighboring class with minimal distance. In such methods, the distance/proximity metric could be the Manhattan (l 1 -norm) distance [7], [5] or cosine similarity [12]. K-means [12], [8] and template matching [6] are the distancebased methods widely used for spike sorting (Fig.…”

Section: B State-of-the-art Bmi Socsmentioning

confidence: 99%

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Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Shaeri¹,

Afzal²,

Shoaran³

2022

Preprint

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Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain machine interfaces (BMIs), with a focus on state-of-theart prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.

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Section: B State-of-the-art Bmi Socsmentioning

confidence: 99%

“…In such methods, the distance/proximity metric could be the Manhattan (l 1 -norm) distance [7], [5] or cosine similarity [12]. K-means [12], [8] and template matching [6] are the distancebased methods widely used for spike sorting (Fig. 2(a)).…”

Section: B State-of-the-art Bmi Socsmentioning

confidence: 99%

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Shaeri¹,

Afzal²,

Shoaran³

2022

Preprint

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“…In [25], a convolutional neural network (CNN) was employed to distinguish between categories of spikes. An L 2 -normalized deep convolutional autoencoder with spike sorting-aware loss was exploited for feature extraction for fully unsupervised and online spike sorting [26]. In [27], a supervised deep learning was used to distinguish spike events from non-neural events, together with deep learning for offline spike sorting [28].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

2023

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In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multiunit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts.INDEX TERMS Artefact removal, channel selection, convolutional neural network (CNN), deep earning, extracellular recordings, real-time sorting, spike sorting, .

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“…Other recent works have explored the application of neural networks to spike sorting [17], [19], [20]. While those implementations can attain high accuracies and robustness to background noise, they typically require supervised training, as well as substantial power and area, particularly when dealing with a large number of channels.…”

Section: Introductionmentioning

confidence: 99%

An Online-Spike-Sorting IC Using Unsupervised Geometry-Aware OSort Clustering for Efficient Embedded Neural-Signal Processing

Chen,

Tacca,

Chen

et al. 2023

IEEE J. Solid-State Circuits

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As neural-recording devices get denser and generate more data due to ever higher channel counts, on-chip and online neural signal processor (NSP) becomes crucial to reduce the data-transmission power and to enable real-time closed-loop applications with minimum latency. For this purpose, we report an online spike-sorting integrated circuit (IC) able to process neural signals from 384 channels with software-comparable accuracy. By combining three main innovations, our design drastically improves the fundamental trade-off between hardware resources and real-time performance. Specifically, a central spike detection (CSD) algorithm is designed to mitigate the impact of redundant spikes on accuracy. Second, a peak first and second derivative extrema (FSDE) method is devised to accomplish robust feature extraction (FE) across various datasets. Finally, to deal with the trade-off between accuracy and complexity in the clustering stage, a geometry-aware OSort (Geo-OSort) algorithm is developed. A prototype chip has been fabricated in a 22 nm FDSOI CMOS process. The measurement results show that the designed NSP achieves an area of 0.0013 mm 2 /channel, a power consumption of 1.78 µW/channel, a latency of 33.9 µs, and an accuracy of 97.7% without clustering pretraining.

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A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

Cited by 15 publications

References 24 publications

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

An Online-Spike-Sorting IC Using Unsupervised Geometry-Aware OSort Clustering for Efficient Embedded Neural-Signal Processing

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