Related papers: TMS-EEG Reliability: Bridging the Gap to Clinical …
Decoding natural language from brain activity using non-invasive electroencephalography (EEG) remains a significant challenge in neuroscience and machine learning, particularly for open-vocabulary scenarios where traditional methods…
In this article, we design a novel Transcranial Electrical Stimulator for medical applications, which is very cheap and can produce the desired signals very accurately. Our fabricated stimulator generates all current signals related to…
Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent advancements in machine learning and generative modeling have catalyzed the application of EEG…
This research addresses a validated TMS EEG cleaning pipeline and a corresponding benchmark dataset. It evaluates two widely used artifact removal pipelines. A reference dataset of carefully preprocessed EEG signals was established to…
Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with…
Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to…
The objective of this work is to develop an Electronic Medical Record (EMR) data processing tool that confers clinical context to Machine Learning (ML) algorithms for error handling, bias mitigation and interpretability. We present…
Transcranial Electrical Stimulation (tES) is a neuromodulation technique that utilizes electrodes on the scalp to stimulate target brain regions. tES has shown promise in treating many neurological conditions, such as stroke rehabilitation…
Clinical electroencephalography is routinely used to evaluate patients with diverse and often overlapping neurological conditions, yet interpretation remains manual, time-intensive, and variable across experts. While automated EEG analysis…
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with…
We expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data…
Objectives: This study examined whether currently existing clinical structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (18FDG-PET) capabilities and board-certified radiologists' reports and…
Conventional scalp-based EEG systems are cumbersome to use, requiring extensive setup, restrictive wiring, and conductive gels that can dry out and limit long-term monitoring, while also carrying social stigma. As a result, there is…
Regeneration of the nervous system after injury remains an important therapeutic objective, especially in the central nervous system (CNS), in which regeneration is restricted by both neuronal limitations as well as adverse extracellular…
In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments,…
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications. Despite advances in predictive performance, the clinical utility of such methods…
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and…
Electroencephalography (EEG) underpins neuroscience, clinical neurophysiology, and brain-computer interfaces (BCIs), yet pronounced inter- and intra-subject variability limits reliability, reproducibility, and translation. This systematic…
Continuous electroencephalography (EEG) is routinely used in neurocritical care to monitor seizures and other harmful brain activity, including rhythmic and periodic patterns that are clinically significant. Although deep learning methods…
Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the…