AIMC Topic: Imagination

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The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 7, 2025
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...
Deep Learning Blinking Parkinson Disease Sleep Deprivation Electroencephalography Imagination Female Signal Processing, Computer-Assisted Adult Humans Male Alzheimer Disease Neural Networks, Computer Algorithms Artifacts
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REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.

IEEE journal of biomedical and health informatics Mar 6, 2025
High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2...
Humans Neural Networks, Computer Algorithms Adult Signal Processing, Computer-Assisted Brain-Computer Interfaces Male Electrodes Imagination Electroencephalography
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Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Sensors (Basel, Switzerland) Feb 25, 2025
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw ...
Movement Imagination Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Neural Networks, Computer Algorithms
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Latent alignment in deep learning models for EEG decoding.

Journal of neural engineering Feb 17, 2025
. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning ...
Sleep Stages Imagination Electroencephalography Brain-Computer Interfaces Event-Related Potentials, P300 Humans Male Deep Learning Female Adult
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Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.

IEEE journal of biomedical and health informatics Feb 10, 2025
In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riema...
Humans Imagination Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted Algorithms Brain-Computer Interfaces Brain
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ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.

Journal of neural engineering Feb 7, 2025
. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical a...
Humans Neural Networks, Computer Brain-Computer Interfaces Movement Imagination Brain Spectroscopy, Near-Infrared Electroencephalography
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Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Transfer learning is one of the popular methods to solve the problem of insufficient data in subject-specific electroencephalogram (EEG) recognition tasks. However, most existing approaches ignore the difference between subjects and transfer the same...
Seizures Pattern Recognition, Automated Imagination Humans Deep Learning Algorithms Neural Networks, Computer Electroencephalography Databases, Factual
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Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Brain research Feb 2, 2025
Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as non...
Imagination Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Neural Networks, Computer Brain Male Deep Learning Convolutional Neural Networks Electroencephalography Humans
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Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.

Brain topography Jan 28, 2025
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, a...
Humans Imagination Neural Networks, Computer Male Brain Electroencephalography Speech Machine Learning Female Brain-Computer Interfaces Adult
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TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly use...
Male Deep Learning Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Female Young Adult Adult Humans Algorithms
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