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Brain-Computer Interfaces

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Robust Transcoding Sensory Information With Neural Spikes.

IEEE transactions on neural networks and learning systems
Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies ...

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain a...

The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review.

Disability and rehabilitation. Assistive technology
PURPOSE: Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to ev...

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Computational intelligence and neuroscience
Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and furt...

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Sensors (Basel, Switzerland)
Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine ...

Decoding ECoG signal into 3D hand translation using deep learning.

Journal of neural engineering
Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals wi...

PlatypOUs-A Mobile Robot Platform and Demonstration Tool Supporting STEM Education.

Sensors (Basel, Switzerland)
Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile ...

A behavioral paradigm for cortical control of a robotic actuator by freely moving rats in a one-dimensional two-target reaching task.

Journal of neuroscience methods
BACKGROUND: Controlling the trajectory of a neuroprosthesis to reach distant targets is a commonly used brain-machine interface (BMI) task in primates and has not been available for rodents yet.

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.

Computational intelligence and neuroscience
Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroen...

Organic electrochemical neurons and synapses with ion mediated spiking.

Nature communications
Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating...