AIMC Topic: Brain-Computer Interfaces

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Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

Clinical EEG and neuroscience
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...

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
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...

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Sensors (Basel, Switzerland)
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...

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Medical & biological engineering & computing
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...

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns.

Sensors (Basel, Switzerland)
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...

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

Sensors (Basel, Switzerland)
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 ...

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

Biomedical engineering online
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...

Machine translation of cortical activity to text with an encoder-decoder framework.

Nature neuroscience
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...

EEG based Classification of Long-term Stress Using Psychological Labeling.

Sensors (Basel, Switzerland)
Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavaila...

Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) e...