AIMC Topic: Electroencephalography

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An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Computers in biology and medicine
Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of deep learning-based approaches have emerged to tackle this challenging task, leading to significant ad...

Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor.

Computers in biology and medicine
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal p...

Novel multimodal sensing and machine learning strategies to classify cognitive workload in laparoscopic surgery.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Surgeons can experience elevated cognitive workload (CWL) during surgery due to various factors including operative technicalities and the environmental demands of the operating theatre. This can result in poorer outcomes and have a detri...

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Neural networks : the official journal of the International Neural Network Society
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...

Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.

IEEE transactions on visualization and computer graphics
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise...

Artificial intelligence and machine learning in disorders of consciousness.

Current opinion in neurology
PURPOSE OF REVIEW: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness followi...

Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lasting from 0.5 to 2 seconds. They are often misdiagnosed due to their atypical presentation, and treatment is frequently delayed, leading to stagnation or...

Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approa...

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifact...

Predicting the effectiveness of binaural beats on working memory.

Neuroreport
Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functio...