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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
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...

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
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...

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
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...

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
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...

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
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 ...

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
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...

EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.

Human brain mapping
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...

Perspective of Signal Processing-Based on Brain-Computer Interfaces Using Machine Learning Methods.

Studies in health technology and informatics
The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play ...

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Cerebral cortex (New York, N.Y. : 1991)
Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging tech...

Error-related Potentials in a Virtual Pick-and-Place Experiment: Toward Real-world Shared-control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In Human-Robot Collaboration setting a robot may be controlled by a user directly or through a Brain-Computer Interface that detects user intention, and it may act as an autonomous agent. As such interaction increases in complexity, conflicts become ...