IEEE journal of biomedical and health informatics
Apr 4, 2025
Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electroencephalogram (EEG) recently. The complexity of EEG data poses a...
Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively ...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 1, 2025
Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Fun...
Annals of the New York Academy of Sciences
Mar 30, 2025
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, fro...
IEEE journal of biomedical and health informatics
Mar 6, 2025
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learn...
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...
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 ...
The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry
Feb 19, 2025
The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ ...
The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain-computer interface (BCI), where the brain activity ...
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