Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface

Qi, Yu; Zhu, Xinyun; Xu, Kedi; Ren, Feixiao; Jiang, Hongjie; Zhang, Jianmin; Pan, Gang; Wang, Yueming

doi:10.1109/tbme.2022.3182588

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…Two 96-channel Utah microelectrode arrays (4 mm × 4 mm, Utah Array with 1.5 mm length, Blackrock Microsystems, Salt Lake City, UT, USA) were implanted in the participant’s left cerebral cortex, one was placed in the hand knob area of M1, and the other was implanted in the arm area of M1 about 2 mm away from the previous one. The specific placement was shown in previous work ( Qi et al, 2022 ). The participant was asked to image hand and elbow movement during fMRI scanning to determine the area before implantation.…”

Section: Methodsmentioning

confidence: 99%

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

Lin

Lai

Wan

et al. 2023

Front. Hum. Neurosci.

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IntroductionIn the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs).MethodsThe LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands.ResultsThe frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8–38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What’s more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135–300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals.DiscussionThe results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology.Clinical trial registrationhttps://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

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Section: Methodsmentioning

confidence: 99%

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

Lin

Lai

Wan

et al. 2023

Front. Hum. Neurosci.

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“…Handwriting, a skill developed through years of deliberate practice, serves as an example of sophisticated fine motor control in humans. This study investigated the neural basis of handwriting by recording single-unit neural activity from human MC with two 96-channel Utah microelectrode arrays in the left hand knob area of the precentral gyrus 14 (Fig. 1a and Extended Data Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1h). We built a state-dependent neural decoder to decode the movement velocity and recovered the handwriting trajectory based on velocity 14 (Extended Data Fig. 4a).…”

Section: Introductionmentioning

confidence: 99%

Human Motor Cortex Encodes Complex Handwriting Through a Sequence of Primitive Neural States

Qi,

Zhu,

Xiong

et al. 2024

Preprint

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How the human motor cortex (MC) orchestrates sophisticated fine movements such as handwriting remains a puzzle1–3. Here, we investigate this question through Utah array recordings from human MC hand knob, during imagined handwriting of Chinese characters (306 characters tested, 6.3 ± 2.0 strokes per character). We find MC programs the writing of complicated characters by sequencing a small set of primitive states: The directional tuning of motor neurons remains stable within each primitive state but strongly varies across states. Furthermore, the occurrence of a primitive state is encoded by a separate set of neurons not directly involved in movement control. By automatically identifying the primitive states and corresponding neuronal tuning properties, we can reconstruct a recognizable writing trajectory for each character (84% improvement in reconstruction accuracy compared with baseline). Our findings unveil that skilled, sophisticated movements are decomposed into a sequence of primitive movements that are programmed through state-specific neural configurations, and this hierarchical control mechanism sheds new light on the design of high-performance brain-computer interfaces.

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“…Traditionally, spike sorting is performed offline, where the sorting process occurs after all the data has been collected. However, with the emergence of closed-loop braincomputer interfaces (BCIs) and in vivo signal analysis, the need for online spike sorting has become crucial [6,7]. In online spike sorting, the spike events need to be classified immediately after the signal is recorded.…”

Section: Introductionmentioning

confidence: 99%

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

Yu,

Qi,

Pan

2023

J. Neural Eng.

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Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort’s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

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Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface

Cited by 14 publications

References 45 publications

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

Human Motor Cortex Encodes Complex Handwriting Through a Sequence of Primitive Neural States

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

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