AIMC Topic: Electroencephalography

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Edge deep learning for neural implants: a case study of seizure detection and prediction.

Journal of neural engineering
Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appro...

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

Computers in biology and medicine
The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, includin...

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal.

Computational and mathematical methods in medicine
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation...

Does agency matter? Neural processing of robotic movements in 4- and 8-year olds.

Neuropsychologia
Despite the increase in interactions between children and robots, our understanding of children's neural processing of robotic movements is limited. The current study theorized that motor resonance hinges on the agency of an actor: its ability to per...

Machine learning for detection of interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis o...

An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and con...

Deep learning for robust detection of interictal epileptiform discharges.

Journal of neural engineering
Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs bas...

A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?

Cognitive, affective & behavioral neuroscience
Social species rely on the ability to modulate feedback-monitoring in social contexts to adjust one's actions and obtain desired outcomes. When being awarded positive outcomes during a gambling task, feedback-monitoring is attenuated when strangers a...

Neurophysiological brain mapping of human sleep-wake states.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to inclu...