AIMC Topic: Imagination

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[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant wor...

Robot or human musicians? The modulating role of perceived performer on how music influences food choices.

Applied psychology. Health and well-being
Previous research has shown that music robots may reshape people's perceptions of music and health-related behaviors. We investigated how the perceived identity of the music performers (humans or robots) influenced people's music-induced mental image...

What could be? Depends on who you ask: Using latent profile analysis and natural language processing to identify the different types and content of utopian visions.

The British journal of social psychology
When people think of a utopian future, what do they imagine? We examined (a) whether people's self-generated utopias differ by how much they criticize, seek to change or escape from an undesirable present; and (b) whether these distinct types of utop...

[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and...

EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.

Journal of integrative neuroscience
BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is...

Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenar...

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
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 ...

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...

Bi-Stream Adaptation Network for Motor Imagery Decoding.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information can thereby enhance network performance by having a comprehensive understanding of MI. Besides, due to...

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 ...