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Imagery, Psychotherapy

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Deep Learning of Motor Imagery EEG Classification for Brain-Computer Interface Illiterate Subject.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
BCI illiterate subject is defined as the subject who cannot achieve accuracy higher than 70%. BCI illiterate subject cannot produce stronger contralateral ERD/ERS activity, thus most of the frequency band-based algorithms cannot obtain higher accurac...

Visual mental imagery: A view from artificial intelligence.

Cortex; a journal devoted to the study of the nervous system and behavior
This article investigates whether, and how, an artificial intelligence (AI) system can be said to use visual, imagery-based representations in a way that is analogous to the use of visual mental imagery by people. In particular, this article aims to ...

EEG Processing to Discriminate Transitive-Intransitive Motor Imagery Tasks: Preliminary Evidences using Support Vector Machines.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
It is known that brain dynamics significantly changes during motor imagery tasks of upper limb involving different kind of interactions with an object. Nevertheless, an automatic discrimination of transitive (i.e., actions involving an object) and in...

DeepMI: Deep Learning for Multiclass Motor Imagery Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In Brain-Computer Interface (BCI) Research,Electroencephalography (EEG) has obtained great attention for biomedical applications. In BCI system, feature representation and classification are important tasks as the accuracy of classification highly de...

Increasing the learning Capacity of BCI Systems via CNN-HMM models.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Despite all the work in the Brain Computer Interface (BCI) community, one of the main issues that prevents it from becoming pervasive is the limitation on the number of commands with a satisfactory accuracy of detection. In this paper, we propose a 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...

Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification.

Neural plasticity
Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency ba...

A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke.

Clinical EEG and neuroscience
Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducing activity-dependent brain plasticity. The purpose of this study was to investigate the efficacy of ...