AIMC Topic: Electroencephalography
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Ongoing brain rhythms shape I-wave properties in a computational model.
Brain stimulation
Mar 20, 2018
BACKGROUND: Responses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity...
Thalamocortical dysrhythmia detected by machine learning.
Nature communications
Mar 16, 2018
Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-freq...
Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG.
Journal of medical systems
Mar 16, 2018
This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. ...
Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.
Scientific reports
Mar 15, 2018
Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG)....
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks.
IEEE transactions on neural networks and learning systems
Mar 9, 2018
Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-compu...
Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.
Journal of neural engineering
Feb 15, 2018
OBJECTIVE: Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG ...
Predicting sex from brain rhythms with deep learning.
Scientific reports
Feb 15, 2018
We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar...
Using Dictionary Pair Learning for Seizure Detection.
International journal of neural systems
Feb 13, 2018
Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) r...
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions.
IEEE transactions on cybernetics
Feb 8, 2018
In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in real-world applications, we design a six-electrode pl...
Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework.
Artificial intelligence in medicine
Feb 1, 2018
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data....
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