Inverse solutions for classifying Event Related Potentials

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An automatic seizure detection method from high-resolution intracranial-EEG (iEEG) signals is presented to minimize the computational complexity and realize real-time accurate seizure detection for biomedical implants. Complex signal processing on a large ...

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 ...

The researchers used a machine-learning classification approach to better understand neurological features associated with periods of wayfinding uncertainty. The participants (n = 30) were asked to complete wayfinding tasks of varying difficulty in a virtu ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two- ...

Objective: Markup of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG markup is a time-consuming, subjective, and the specialized task where the human r ...

Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...

Objective. Detection and early prediction of mental fatigue (i.e. shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue. ...

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) meth ...

Visual motion discrimination involves reciprocal interactions in the alpha band between the primary visual cortex (V1) and mediotemporal areas (V5/MT). We investigated whether modulating alpha phase synchronization using individualized multisite transcrani ...

This study presents a new methodology for obtaining functional brain networks (FBNs) using multichannel scalp EEG recordings. The developed methodology extracts pair-wise phase synchrony between EEG electrodes to obtain FBNs at delta, theta, and alpha-band ...

Inverse solutions for classifying Event Related Potentials

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Computation Complexity Reduction Technique for Accurate Seizure Detection Implants

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

Identifying uncertainty states during wayfinding in indoor environments: An EEG classification Study

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Predicting task performance from biomarkers of mental fatigue in global brain activity

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Enhancing visual motion discrimination by desynchronizing bifocal oscillatory activity

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