Adaptive common average reference for in vivo multichannel local field potentials

Liu, Xinyu; Wan, Hong; Li, Shan; Chen, Yan; Shi, Li

doi:10.1007/s13534-016-0004-1

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…This demonstrates the effectiveness of LSTM decoder in capturing nonlinear and complex relationship between LFPs and hand kinematics. On the other hand, KF decoder with its inherent assumptions results in poor performance, especially for LFPs that exhibit high spatial correlation [14]. LFPs recorded from unipolar reference are often contaminated by spatially correlated noises, which affects the decoding performance.…”

Section: Discussionmentioning

confidence: 99%

“…LFPs were obtained by low pass filtering raw neural data using 4 th order Butterworth filter at 300 Hz and then downsampling them to 1 kHz. LFPs have been shown to contain common noises, partly arising from the use of a single, distal reference (unipolar) [14]. To remove these common noises, we performed common average reference (CAR) and subtracted it from LFP signal in each channel.…”

Section: B Neural Signal Processingmentioning

confidence: 99%

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Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

Ahmadi

Constandinou

Bouganis

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Local field potential (LFP) has gained increasing interest as an alternative input signal for brain-machine interfaces (BMIs) due to its informative features, long-term stability, and low frequency content. However, despite these interesting properties, LFP-based BMIs have been reported to yield low decoding performances compared to spike-based BMIs. In this paper, we propose a new decoder based on long short-term memory (LSTM) network which aims to improve the decoding performance of LFP-based BMIs. We compare offline decoding performance of the proposed LSTM decoder to a commonly used Kalman filter (KF) decoder on hand kinematics prediction tasks from multichannel LFPs. We also benchmark the performance of LFP-driven LSTM decoder against KF decoder driven by two types of spike signals: singleunit activity (SUA) and multi-unit activity (MUA). Our results show that LFP-driven LSTM decoder achieves significantly better decoding performance than LFP-, SUA-, and MUAdriven KF decoders. This suggests that LFPs coupled with LSTM decoder could provide high decoding performance, robust, and low power BMIs.

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Section: Discussionmentioning

confidence: 99%

Section: B Neural Signal Processingmentioning

confidence: 99%

Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

Ahmadi

Constandinou

Bouganis

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…The spike signal was removed from the raw signal using a 0.25–5 kHz second‐order Butterworth bandpass filter at a sampling rate of 30 kHz. The 50‐Hz power frequency interference and spatial artifact noise in the LFP signal were removed using a least mean square adaptive filter and an adaptive standard average reference filter, 31 respectively. Spike was extracted from the raw signal using threshold detection and classified using the Skew‐t algorithm 32 .…”

Section: Methodsmentioning

confidence: 99%

Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

Liu

Ren

et al. 2022

Anim Models and Exp Med

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Background: Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear.Methods: Multi-array electrodes were implanted in the dPAG nuclei of C57BL/6 mice.Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed.

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“…A Local Common Average Referencing (L-CAR) filter was then applied to the recorded neural voltages to remove correlated noise common across adjacent channels (Ludwig et al 2009; Xinyu et al 2017). The purpose of L-CARS was to remove large-area common noise contaminating the recording channel to allow better spike isolation.…”

Section: Methodsmentioning

confidence: 99%

Multichannel neural spike sorting with spike reduction and positional feature

Mohammadi

Denman

Klug

et al. 2022

Preprint

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Sorting neural voltages measured from a multichannel neural probe to extract the single unit activities of neuronal firing, especially in real-time, remains a significant technical challenge, largely due to the large amount of acquired data and the technical difficulties involved in processing and classifying these neural spikes promptly. Most neural spike sorting algorithms focus on sorting neural spikes post hoc for high sorting accuracy, and reducing the processing time generally is not the chief concern. Here we report on two signal processing modifications to our previously developed single-channel real-time spike sorting (Enhanced Growing Neural Gas) algorithm, which is largely based on graph network. Duplicated neural spikes were eliminated and represented by the neural spike with the strongest signal profile, significantly reducing the amount of neural data to be processed. In addition, the channel from which the representing neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. With these two modifications, the Graph nEtwork Multichannel (GEMsort) neural spike sorting algorithm can rapidly sort neural spikes without requiring significant computer processing power and system memory storage. The parallel processing architecture of GEMsort is particularly suitable for digital hardware implementation to improve processing speed and recording channel scalability. Multichannel synthetic neural spikes and actual neural recordings with Neuropixels probes were used to evaluate the sorting accuracies of the GEMsort algorithm.

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Adaptive common average reference for in vivo multichannel local field potentials

Cited by 24 publications

References 21 publications

Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

Multichannel neural spike sorting with spike reduction and positional feature

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