Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledg...
Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived...
Physical and engineering sciences in medicine
Oct 22, 2020
Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the ...
To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly contrast brain activity during study of later-remembered versus later-forgotten items. This "subseque...
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Oct 15, 2020
OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsE...
OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature o...
OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep g...
OBJECTIVE: Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal ...
Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging...
The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classificatio...
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