AIMC Topic: Electroencephalography

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Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition.

Computational intelligence and neuroscience Sep 19, 2017
This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four...
Humans Brain Support Vector Machine Emotions Young Adult Adult Electroencephalography Facial Expression
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A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot.

Journal of healthcare engineering Sep 17, 2017
OBJECTIVE: This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility.
Rehabilitation Ankle Photic Stimulation Reproducibility of Results Fourier Analysis Signal Processing, Computer-Assisted Humans Brain-Computer Interfaces Young Adult Virtual Reality Feasibility Studies Algorithms Evoked Potentials, Visual Motion Male Robotics Female Ankle Joint Healthy Volunteers Electroencephalography
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New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems.

Computational intelligence and neuroscience Sep 11, 2017
Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two...
Electroencephalography Heart Heart Diseases Brain Humans Tomography, Emission-Computed, Single-Photon Computer Simulation Neural Networks, Computer Visual Perception Blood Transfusion Machine Learning Taraxacum
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Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 1, 2017
Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the class...
Reproducibility of Results Support Vector Machine Fuzzy Logic Humans Software Algorithms Seizures Supervised Machine Learning Pattern Recognition, Automated Transfer, Psychology Models, Statistical Electroencephalography Epilepsy
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Identifying sleep spindles with multichannel EEG and classification optimization.

Computers in biology and medicine Sep 1, 2017
Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater ag...
Adult Male Adolescent Signal Processing, Computer-Assisted Sleep Machine Learning Models, Neurological Humans Databases, Factual Electroencephalography Female
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Author response to: Stereotactic accuracy must be as high as possible in stereoelectroencephalography procedures.

Journal of robotic surgery Aug 31, 2017
Humans Robotics Stereotaxic Techniques Rosa Electroencephalography Robotic Surgical Procedures
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Cognitive-Based EEG BCIs and Human Brain-Robot Interactions.

Computational intelligence and neuroscience Aug 28, 2017
Brain-Computer Interfaces Brain Robotics Electroencephalography Humans
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Transfer Learning: A Riemannian Geometry Framework With Applications to Brain-Computer Interfaces.

IEEE transactions on bio-medical engineering Aug 21, 2017
OBJECTIVE: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are conside...
Models, Theoretical Electroencephalography Brain-Computer Interfaces Humans Machine Learning Databases, Factual
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A New Method for Automatic Sleep Stage Classification.

IEEE transactions on biomedical circuits and systems Aug 14, 2017
Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for autom...
Signal Processing, Computer-Assisted Sleep Stages Humans Neural Networks, Computer Machine Learning Algorithms Models, Theoretical Electroencephalography
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Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.

Journal of neuroscience methods Aug 12, 2017
BACKGROUND: The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch p...
Machine Learning Brain Female Humans Models, Neurological Electroencephalography Markov Chains Neuropsychological Tests Young Adult Male Neuronal Plasticity Speech Perception Pitch Perception
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