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Brain-Computer Interfaces

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Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expression...

Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.

Sensors (Basel, Switzerland)
Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled i...

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

Journal of neuroscience methods
BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distribut...

CTNet: a convolutional transformer network for EEG-based motor imagery classification.

Scientific reports
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the tr...

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society
In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring...

Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.

SLAS technology
One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatl...

Advanced rehabilitation in ischaemic stroke research.

Stroke and vascular neurology
At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke, the mortality of patients has been greatly reduced but the number of disabled survivors is increasing, and most of them are elderly patients. Physic...

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

IEEE transactions on cybernetics
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. De...

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

IEEE journal of translational engineering in health and medicine
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the eff...