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Brain Computer Interface

A Brain-Computer Interface (BCI), also known as a Brain-Machine Interface (BMI), is a technology that enables direct communication between the brain and an external device, such as a computer or a machine, without the need for any muscular or peripheral nerve activity. Essentially, BCIs establish a direct pathway between the brain and an external device, allowing for bidirectional communication.

BCIs typically work by detecting and interpreting brain signals, which are then translated into commands that control external devices or provide feedback to the user. These brain signals can be detected through various methods, including electroencephalography (EEG), which measures electrical activity in the brain through electrodes placed on the scalp, or invasive techniques such as implanted electrodes.

Papers

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Showing 191–200 of 466 papers

TitleStatusHype
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface—0
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
EmoWrite: A Sentiment Analysis-Based Thought to Text Conversion -- A Validation Study—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals—0
Enhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
Ensemble Classifier for Eye State Classification using EEG Signals—0
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition—0
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