Continuous decoding of hand kinematics has been recently explored for the intuitive control
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …

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 …

Perception of social stimuli (faces and bodies) relies on “holistic”(ie, global) mechanisms, as
supported by picture-plane inversion: perceiving inverted faces/bodies is harder than …

Facilitating data sharing in scientific research, especially in the domain of animal studies,
holds immense value, particularly in mitigating distress and enhancing the efficiency of data …

The posterior parietal cortex (PPC) serves as a crucial hub for the integration of sensory with
motor cues related to voluntary actions. Visual input is used in different ways along the …

Objective. Motor decoding is crucial to translate the neural activity for brain-computer
interfaces (BCIs) and provides information on how motor states are encoded in the brain …

Deep neural networks (DNNs) are widely adopted to decode motor states from both non-
invasively and invasively recorded neural signals, eg, for realizing brain-computer …

Introduction Adaptation and learning have been observed to contribute to the acquisition of
new motor skills and are used as strategies to cope with changing environments. However, it …

Neural decoding is crucial to translate the neural activity for Brain-Computer Interfaces
(BCIs) and provides information on how external variables (eg, movement) are represented …

Deep convolutional neural networks (DCNN) have made significant progress in a wide
range of applications in recent years, which include image identification, audio recognition …