The science and engineering behind sensitized brain-controlled bionic hands
C Pandarinath, SJ Bensmaia - Physiological Reviews, 2022 - journals.physiology.org
Advances in our understanding of brain function, along with the development of neural
interfaces that allow for the monitoring and activation of neurons, have paved the way for …
interfaces that allow for the monitoring and activation of neurons, have paved the way for …
Neural decoding for intracortical brain–computer interfaces
Brain–computer interfaces have revolutionized the field of neuroscience by providing a
solution for paralyzed patients to control external devices and improve the quality of daily …
solution for paralyzed patients to control external devices and improve the quality of daily …
Brain control of bimanual movement enabled by recurrent neural networks
Brain-computer interfaces have so far focused largely on enabling the control of a single
effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion …
effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion …
A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
Biomedical signals carry signature rhythms of complex physiological processes that control
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface
Modern brain-machine interfaces can return function to people with paralysis, but current
upper extremity brain-machine interfaces are unable to reproduce control of individuated …
upper extremity brain-machine interfaces are unable to reproduce control of individuated …
Continual learning of a transformer-based deep learning classifier using an initial model from action observation EEG data to online motor imagery classification
PL Lee, SH Chen, TC Chang, WK Lee, HT Hsu… - Bioengineering, 2023 - mdpi.com
The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that
enables users to communicate with external environments through their minds. However …
enables users to communicate with external environments through their minds. However …
Balancing memorization and generalization in RNNs for high performance brain-machine interfaces
Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are
currently limited by the accuracy of real-time decoding algorithms. Recurrent neural …
currently limited by the accuracy of real-time decoding algorithms. Recurrent neural …
Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates
Direct electronic communication with sensory areas of the neocortex is a challenging
ambition for brain-computer interfaces. Here, we report the first successful neural decoding …
ambition for brain-computer interfaces. Here, we report the first successful neural decoding …
An in-silico framework for modeling optimal control of neural systems
B Rueckauer, M van Gerven - Frontiers in Neuroscience, 2023 - frontiersin.org
Introduction Brain-machine interfaces have reached an unprecedented capacity to measure
and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor …
and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor …
An energy-efficient spiking neural network for finger velocity decoding for implantable brain-machine interface
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility
augmentation. High-accuracy and low-power algorithms are required to achieve implantable …
augmentation. High-accuracy and low-power algorithms are required to achieve implantable …