Deep Learning-Based Markerless Hand Tracking for Freely Moving Non-Human Primates in Brain–Machine Interface Applications

Electronics, Год журнала: 2025, Номер 14(5), С. 920 - 920

Опубликована: Фев. 26, 2025

The motor cortex of non-human primates plays a key role in brain–machine interface (BMI) research. In addition to recording cortical neural signals, accurately and efficiently capturing the hand movements experimental animals under unconstrained conditions remains challenge. Addressing this challenge can deepen our understanding application BMI behavior from both theoretical practical perspectives. To address issue, we developed deep learning framework that combines Yolov5 RexNet-ECA reliably detect joint positions freely moving at different distances using single camera. model simplifies setup procedure while maintaining high accuracy, with an average keypoint detection error less than three pixels. Our method eliminates need for physical markers, ensuring non-invasive data collection preserving natural subjects. proposed system exhibits accuracy ease use compared existing methods. By quickly acquiring spatiotemporal behavioral metrics, provides valuable insights into dynamic interplay between functions, further advancing

Язык: Английский

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MAIS: an in-vitro sandbox enables adaptive neuromodulation via scalable neural interfaces

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Abstract Brain-machine interfaces (BMIs) predominantly rely on static digital architectures to decode biological neuronal networks, a paradigm that is incompatible with natural neural coding in the human brain 1–4 . Bridging this gap critical step combating dysfunction, enhancing functionality, and refining precision of neuroprosthetics 5 The integration organoids microelectrode array (MEA), as class BMIs, offers humanized vitro platform unique compatibility advantages for dynamic decoding. This study resolves biological-electronic encoding incompatibility organoid-MEA Integration through three progressive breakthroughs. First, human-machine hybrid agent developed newly proposed bioengineered couples together high-density MEAs computational chips, enabling closed-loop perturbation networks via exogenous signals. Second, plasticity-driven real-time tracking activity, we establish dynamically reconfigurable stimulation nodes self-align electrophysiological states organoids. exogenous-endogenous mismatch by implementing adaptation principles ensure spatially adaptive coordination. Finally, shared plasticity rules rather than centralized control, construct first scalable multi-agent interaction system (MAIS) demonstrate its real-world applications. Through designed scenarios pathological/normal network interaction, validate MAIS achieves stable cross-network embodies self-evolving sandbox which decoding bridges gaps between systems, providing foundational infrastructure human-centered interfaces.

Язык: Английский

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Should bionic limb control mimic the human body? Impact of control strategy on bionic hand skill learning

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Фев. 8, 2023

ABSTRACT A longstanding engineering ambition has been to design anthropomorphic bionic limbs: devices that look like and are controlled in the same way as biological body (biomimetic). The untested assumption is biomimetic motor control enhances device embodiment, learning, generalization, automaticity. To test this, we compared non-biomimetic strategies for able-bodied participants when learning operate a wearable myoelectric hand. We across days behavioural tasks two training groups: Biomimetic (mimicking desired hand gesture with hand) Arbitrary (mapping an unrelated gesture). For both trained groups, improved limb control, reduced cognitive reliance, increased embodiment over users had more intuitive faster early training. matched performance later Further, arbitrary showed generalization novel strategy. Collectively, our findings suggest provide different benefits. optimal strategy likely not strictly biomimetic, but rather flexible within spectrum, depending on user, available opportunities user requirements.

Язык: Английский

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Superior cortical venous anatomy for endovascular device implantation: a systematic review

Journal of NeuroInterventional Surgery, Год журнала: 2024, Номер unknown, С. jnis - 021434

Опубликована: Март 27, 2024

Endovascular electrode arrays provide a minimally invasive approach to access intracranial structures for neural recording and stimulation. These are currently used as brain-computer interfaces (BCIs) deployed within the superior sagittal sinus (SSS), although cortical vein implantation could improve quality quantity of recorded signals. However, anatomy veins is heterogenous poorly characterised. MEDLINE Embase databases were systematically searched from inception December 15, 2023 studies describing veins. A total 28 included: 19 cross-sectional imaging studies, six cadaveric one intraoperative anatomical study review. There was substantial variability in diameter, length, confluence angle, location relative underlying cortex. The mean number SSS branches ranged 11 45. Trolard most often reported largest vein, with diameter ranging 2.1 mm 3.3 mm. identified posterior central sulcus. One found significant age-related another myoendothelial sphincters at base Cortical data limited inconsistent. tributary SSS; however, its relation cortex variable. Variability may necessitate individualized pre-procedural planning training decoding endovascular BCI. Future focus on cortex, sulcal vessels, vessel wall required.

Язык: Английский

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Deciphering brain activation during wrist movements: comparative fMRI and fNIRS analysis of active, passive, and imagery states

Experimental Brain Research, Год журнала: 2024, Номер 243(1)

Опубликована: Дек. 31, 2024

Язык: Английский

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