BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effec...
The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional ...
Neural networks : the official journal of the International Neural Network Society
Jan 7, 2025
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. Th...
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNN...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant a...
IEEE journal of biomedical and health informatics
Jan 7, 2025
Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource dem...
BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Theref...
BMC medical informatics and decision making
Jan 6, 2025
BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analys...
By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and n...
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