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Imagery, Psychotherapy

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Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

Scientific reports
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Poss...

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, ...

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

PloS one
High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the ...

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.

Journal of neural engineering
Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely ...

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification d...

A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

Biomedical physics & engineering express
More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify th...

A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images.

Sensors (Basel, Switzerland)
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large ...

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 ...

A neural network-based optimal spatial filter design method for motor imagery classification.

PloS one
In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classificat...