AIMC Topic: Imagery, Psychotherapy

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Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.

PloS one
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Journal of neural engineering
Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing ...

Computational discrimination between natural images based on gaze during mental imagery.

Scientific reports
When retrieving image from memory, humans usually move their eyes spontaneously as if the image were in front of them. Such eye movements correlate strongly with the spatial layout of the recalled image content and function as memory cues facilitatin...

Convolutional neural networks and genetic algorithm for visual imagery classification.

Physical and engineering sciences in medicine
Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the pr...

Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.

PloS one
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the...

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Medical & biological engineering & computing
Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectru...

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.

Sensors (Basel, Switzerland)
Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability f...

Multi optimized SVM classifiers for motor imagery left and right hand movement identification.

Australasian physical & engineering sciences in medicine
EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG s...

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.

Computational intelligence and neuroscience
Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most o...

Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics.

Physics of life reviews
Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to 'action' that do not cause any overt movement. Indeed for any complex body, human or e...