Depression is a heterogeneous disorder marked by disruptions in cognitive and affective processing. While self-reported measures and clinical interviews remain the diagnostic standard, integrating objective neurophysiological markers could enhance as...
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by impairments in executive functioning, particularly response inhibition (RI). This study combines time-domain analysis and source analyses of event-related potentials (ERP...
Detecting driver drowsiness through electroencephalogram (EEG) poses challenges due to the complexity and variability of brain activity across different subjects. This study proposes a feature extraction pipeline combined with a Long Short-Term Memor...
A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly im...
Electroencephalography (EEG) preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE d...
. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning m...
. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often lim...
Major Depressive Disorder is the most prominent global mental health issue impacting millions of individuals worldwide. Electroencephalogram signals capturing intricate brain dynamics have emerged as a promising modality for predicting depression. Th...
Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges ...
Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI)...
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