Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface

Zhang, Yiwei; Wan, Zijun; Wan, Guihua; Zheng, Qi; Chen, Weidong; Zhang, Shaomin

doi:10.1109/embc46164.2021.9630212

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…The differences between control types in M1 LFP may also be related to changes in the properties of individual M1 neuron activity. Distinct changes in neural activity between BMI and manual control have been previously observed in single-and multi-unit motor cortical activity, in the form of changes in preferred direction and modulation depth 29,[48][49][50][51] . The differences we observed between BMI control and manual control in M1 LFP may reflect or drive these distinct single-and multi-unit properties across control types.…”

Section: Discussionmentioning

confidence: 72%

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Zippi,

Shvartsman,

Vendrell-Llopis

et al. 2023

Preprint

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Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

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Section: Discussionmentioning

confidence: 72%

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Zippi,

Shvartsman,

Vendrell-Llopis

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Dataset C:in-vivo real recordings from a non-human primate. We also compared the performance of spike sorting algorithms on in-vivo recordings from a macaque performing a center-out task in a previous study 46 . All methods involved in the experiment on macaques are reported in accordance with the ARRIVE guidelines.…”

Section: Discussionmentioning

confidence: 99%

“…And on the real dataset HC1 44 , the comparison further illustrates the robustness of LDA-DP under low SNR. We finally evaluated the algorithm on real data from non-human primates 46 . The LDA-DP presents a more stable performance, especially under low SNR.…”

Section: Discussionmentioning

confidence: 99%

A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks

Zhang¹,

Liu²,

Yang³

et al. 2022

Sci Rep

Self Cite

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Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.

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“…We also compared the performance of spike sorting algorithms on in-vivo recordings from a macaque performing a center-out task in a previous study [46]. The in-vivo data were collected from a 96-channel Blackrock microelectrode array by using a commercial data acquisition system (Blackrock Microsystem, USA).…”

Section: Discussionmentioning

confidence: 99%

“…And on the real dataset HC1 [44], the comparison further illustrates the robustness of LDA-DP under low SNR. We finally evaluated the algorithm on real data from non-human primates [46]. The performance of LDA-DP also exceeds other algorithms when facing more complex data distributions.…”

Section: Discussionmentioning

confidence: 99%

A Robust Spike Sorting Method based on the Joint Optimization of Linear Discrimination Analysis and Density Peaks

Zhang

Liu²,

Yang³

et al. 2022

Preprint

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ObjectiveExtracellular electrical recording of neural activity is an essential tool in neuroscience, and spike sorting is a fundamental step to process the recordings. Thus it requires spike sorting algorithms to perform robustly in the face of noise and perturbation. A few algorithms have been proposed to overcome these challenges and difficulties. However, when noise level or waveform similarity becomes relatively high, their robustness still faces big challenges.ApproachHere, we propose a spike sorting method, using Linear Discriminant Analysis (LDA) for feature extraction and Density Peaks (DP) for clustering. Since DP well adapts to the diverse distribution of spikes, LDA in our method can concentrate on features associated with clustering according to the feedback conveyed by DP and ignores the unassociated features. Through a combination of LDA and DP, our method can maintain a highly robust performance in various complex data situations.Main resultsIn this study, we compete the proposed density-peaks-based framework with several algorithms. It has demonstrated its high level of sorting accuracy and cluster quality, and outperforms previously established methods in both simulated and real extracellular recordings.SignificanceDue to the rapid development of acquisition and recording technology, neuroscience involves more and more complex and precise signal analysis and decoding research, which makes it imperative to adopt one highly robust spike sorting method for preprocessing. Through evaluations, it has been explicitly shown that the proposed algorithm has strong robustness under high noise levels and high similarity of spike waveforms, and can meet research requirements.

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Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface

Cited by 4 publications

References 14 publications

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks

A Robust Spike Sorting Method based on the Joint Optimization of Linear Discrimination Analysis and Density Peaks

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