Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task spe...
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...
In recent years, there has been a significant increase in research activity on electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCI) in the field of deep learning. However, despite achieving high accuracy, the size of mo...
Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neurosci...
Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalabilit...
Neural decoding of speech intention could advance the development and application of brain-computer interface (BCI) technology. Currently, lack of dataset limited the research on decoding the true speech intention, especially the diverse intentions e...
International journal of neural systems
Oct 18, 2025
Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming a...
Spike sorting, which involves detecting and attributing spikes to their putative neurons from extracellular recordings, is a common process in electrophysiology and brain-computer interface systems. Recent advances in large-scale neural recording tec...
. Deep learning has emerged as a powerful approach for decoding electroencephalography (EEG)-based brain-computer interface (BCI) signals. However, its effectiveness is often limited by the scarcity and variability of available training data. Existin...
Brain-machine interface (BMI) research has shown the efficacy of using motor and sensory-related neural signals to assist physically impaired patients. Despite the comparable ability to extract more abstract cognitive signals from the brain, little e...
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