Clinical neuroimaging in epileptic patients with autoscopic hallucinations and out-of-body experiences

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Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

Background: Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable ...

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the cri ...

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as t ...

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Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

A computer-implemented method for inducing an Out of Body Experience (OBE) in a user through an augmented/virtual reality (AR/VR) system, the OBE including an exit state and a disembodiment state, the method comprising the steps of (a) changing the user vi ...

Recent studies employing body illusions have shown that multisensory conflict can alter body representations and modulate low-level sensory processing. One defining feature of these body illusions is that they are sensory driven and thus passive on behalf ...

Clinical neuroimaging in epileptic patients with autoscopic hallucinations and out-of-body experiences

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INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

First-person experience cannot rescue causal structure theories from the unfolding argument

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

Interpreting deep learning models for epileptic seizure detection on EEG signals

Method and system for creating an out-of-body experience

Imagined paralysis alters somatosensory evoked-potentials