1. Getting a grasp on BMIs: Decoding prehension and speech signals
Laura R Edmondson, Hannes P Saal Neuron. 2022 Jun 1;110(11):1743-1745. doi: 10.1016/j.neuron.2022.05.004.
Wandelt et al. (2022) show that different grasps can be decoded from neural activity in the human supramarginal gyrus (SMG), ventral premotor cortex, and somatosensory cortex during motor imagery and speech, highlighting the attractiveness of higher-level areas such as the SMG for brain-machine interface applications.
2. From grasp to language: embodied concepts and the challenge of abstraction
Michael A Arbib J Physiol Paris. 2008 Jan-May;102(1-3):4-20. doi: 10.1016/j.jphysparis.2008.03.001. Epub 2008 Mar 18.
The discovery of mirror neurons in the macaque monkey and the discovery of a homologous "mirror system for grasping" in Broca's area in the human brain has revived the gestural origins theory of the evolution of the human capability for language, enriching it with the suggestion that mirror neurons provide the neurological core for this evolution. However, this notion of "mirror neuron support for the transition from grasp to language" has been worked out in very different ways in the Mirror System Hypothesis model [Arbib, M.A., 2005a. From monkey-like action recognition to human language: an evolutionary framework for neurolinguistics (with commentaries and author's response). Behavioral and Brain Sciences 28, 105-167; Rizzolatti, G., Arbib, M.A., 1998. Language within our grasp. Trends in Neuroscience 21(5), 188-194] and the Embodied Concept model [Gallese, V., Lakoff, G., 2005. The brain's concepts: the role of the sensory-motor system in reason and language. Cognitive Neuropsychology 22, 455-479]. The present paper provides a critique of the latter to enrich analysis of the former, developing the role of schema theory [Arbib, M.A., 1981. Perceptual structures and distributed motor control. In: Brooks, V.B. (Ed.), Handbook of Physiology--The Nervous System II. Motor Control. American Physiological Society, pp. 1449-1480].
3. Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG
Shuo Wang, Jingjing Zheng, Bin Zheng, Xianta Jiang Biosensors (Basel). 2022 Jan 21;12(2):57. doi: 10.3390/bios12020057.
Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a sweet period before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.
