AIMC Topic: Brain-Computer Interfaces

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Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning ...
Feedback Reward Brain-Computer Interfaces Humans Machine Learning Algorithms Reinforcement, Psychology
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Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). W...
Movement Signal Processing, Computer-Assisted Hand Brain-Computer Interfaces Deep Learning Female Hand Strength Humans Adult Electroencephalography Algorithms Male
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Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks ...
Humans Neural Networks, Computer Algorithms Brain Convolutional Neural Networks Imagination Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces
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Artificial touch feedback using microstimulation of human somatosensory cortex to convey grip force from a robotic hand.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living wi...
Somatosensory Cortex Touch Feedback, Sensory Male Humans Hand Strength Adult Hand Brain-Computer Interfaces Electric Stimulation Robotics
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Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminat...
Brain-Computer Interfaces Evoked Potentials, Visual Algorithms Adult Male Electroencephalography Signal Processing, Computer-Assisted Calibration Humans Machine Learning
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EEG Acquisition and Motor Imagery Classification for Robotic Control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating th...
Machine Learning Support Vector Machine Robotics Humans Movement Imagination Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces
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A Method of Cross-Subject Transfer Learning for Ultra Short Time SSVEP Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The steady-state visual evoked potentials (SSVEP) based brain-computer interfaces (BCIs) require extensive training data for efficient classification, but existing algorithms struggle with ultra short time inputs (less than 0.2 seconds), limiting the...
Humans Brain-Computer Interfaces Evoked Potentials, Visual Time Factors Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted Machine Learning Algorithms
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Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer ...
Imagination Electroencephalography Humans Signal Processing, Computer-Assisted Machine Learning Brain-Computer Interfaces Algorithms Speech
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EEG Emotion Recognition Based on 3D-CTransNet.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structur...
Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Algorithms Deep Learning Emotions
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EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Human brain mapping Jun 15, 2024
Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG usi...
Male Magnetic Resonance Imaging Electroencephalography Brain-Computer Interfaces Female Humans Neurofeedback Young Adult Neural Networks, Computer Sensorimotor Cortex Adult
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