AIMC Topic: Space Perception

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Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically, a large amount of training data needs to ...

Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload ...

Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience.

Neural networks : the official journal of the International Neural Network Society
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remai...

Neuronal network-based mathematical modeling of perceived verticality in acute unilateral vestibular lesions: from nerve to thalamus and cortex.

Journal of neurology
Acute unilateral lesions of vestibular graviceptive pathways from the otolith organs and semicircular canals via vestibular nuclei and the thalamus to the parieto-insular vestibular cortex regularly cause deviations of perceived verticality in the fr...

How Much Does Movement and Location Encoding Impact Prefrontal Cortex Activity? An Algorithmic Decoding Approach in Freely Moving Rats.

eNeuro
Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other typ...

Pseudoneglect in Visual Search: Behavioral Evidence and Connectional Constraints in Simulated Neural Circuitry.

eNeuro
Most people tend to bisect horizontal lines slightly to the left of their true center (pseudoneglect) and start visual search from left-sided items. This physiological leftward spatial bias may depend on hemispheric asymmetries in the organization of...

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

Computational intelligence and neuroscience
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on...

Deep learning with convolutional neural networks for EEG decoding and visualization.

Human brain mapping
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but...

Qualitative spatial logic descriptors from 3D indoor scenes to generate explanations in natural language.

Cognitive processing
The challenge of describing 3D real scenes is tackled in this paper using qualitative spatial descriptors. A key point to study is which qualitative descriptors to use and how these qualitative descriptors must be organized to produce a suitable cogn...