AIMC Topic: Brain-Computer Interfaces

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

PloS one Jun 11, 2020
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...
Adult Humans Brain-Computer Interfaces Male Female Imagery, Psychotherapy Deep Learning Motor Activity Young Adult Middle Aged Electroencephalography Signal Processing, Computer-Assisted
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Machine-learning-based diagnostics of EEG pathology.

NeuroImage Jun 10, 2020
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding ...
Machine Learning Databases, Factual Brain Female Humans Brain Diseases Brain-Computer Interfaces Middle Aged Infant Adolescent Electroencephalography Signal Processing, Computer-Assisted Child, Preschool Child Young Adult Adult Male Infant, Newborn Aged Aged, 80 and over
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Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

Clinical EEG and neuroscience Jun 3, 2020
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effect...
Neural Networks, Computer Brain Female Adult Humans Diagnosis, Computer-Assisted Deep Learning Electroencephalography Brain-Computer Interfaces Male Middle Aged Depressive Disorder, Major
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Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.

Neural networks : the official journal of the International Neural Network Society May 29, 2020
Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. N...
Movement Electroencephalography Imagination Brain-Computer Interfaces Humans Machine Learning
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Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Sensors (Basel, Switzerland) May 18, 2020
Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first incl...
Brain-Computer Interfaces Manufacturing Industry Brain Deep Learning Electroencephalography Humans
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EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Medical & biological engineering & computing May 11, 2020
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...
Signal Processing, Computer-Assisted Hand Healthy Volunteers Male Support Vector Machine Electroencephalography Brain-Computer Interfaces Imagery, Psychotherapy Humans Female Young Adult Machine Learning Algorithms
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Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns.

Sensors (Basel, Switzerland) Apr 24, 2020
Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a mo...
Discriminant Analysis Humans Recognition, Psychology Neural Networks, Computer Signal Processing, Computer-Assisted Algorithms Brain-Computer Interfaces Robotics
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Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

Sensors (Basel, Switzerland) Apr 23, 2020
The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using ...
Confidence Intervals Brain-Computer Interfaces Wavelet Analysis Algorithms Support Vector Machine Humans Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted Machine Learning
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A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

Biomedical engineering online Apr 16, 2020
BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such cali...
Probability Calibration Brain-Computer Interfaces Time Factors Signal Processing, Computer-Assisted Humans Electroencephalography Electrodes Machine Learning
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Machine translation of cortical activity to text with an encoder-decoder framework.

Nature neuroscience Mar 30, 2020
A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent a...
Female Adult Middle Aged Humans Speech Perception Neural Networks, Computer Electrocorticography Speech Brain Brain-Computer Interfaces
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