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A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity. Such real-world data is massive, diverse,…

Machine Learning · Computer Science 2024-11-22 Lucas Correia , Jan-Christoph Goos , Philipp Klein , Thomas Bäck , Anna V. Kononova

Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neural activity. In this work, we investigate whether features derived from topological data…

Machine Learning · Computer Science 2026-04-15 Sunia Tanweer , Narayan Puthanmadam Subramaniyam , Firas A. Khasawneh

An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrograms are conventionally projected into the episode-wise…

Signal Processing · Electrical Eng. & Systems 2022-04-22 Zheng Chen , Lingwei Zhu , Ziwei Yang , Renyuan Zhang

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to…

The diagnosis of epilepsy generally includes a visual inspection of EEG recorded data by the Neurologist, with the purpose of checking the occurrence of transient waveforms called interictal epileptiform discharges. These waveforms have…

Computer Vision and Pattern Recognition · Computer Science 2016-06-08 Andre Rosado , Agostinho C Rosa

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and…

The electrocardiogram (ECG) is an inexpensive and widely available tool for cardiac assessment. Despite its standardized format and small file size, the high complexity and inter-individual variability of ECG signals (typically a…

Machine Learning · Computer Science 2025-08-04 Christopher Harvey , Sumaiya Shomaji , Zijun Yao , Amit Noheria

Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpretability limits of current deep learning approaches.…

Machine Learning · Computer Science 2025-09-09 John Tolladay , Beth Albert , Gabriel Davis Jones

Intracranial hemorrhage (ICH) secondary to Traumatic Brain Injury (TBI) represents a critical diagnostic challenge, with approximately 64,000 TBI-related deaths annually in the United States. Current diagnostic modalities including Computed…

Image and Video Processing · Electrical Eng. & Systems 2025-10-27 Phat Tran , Enbai Kuang , Fred Xu

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…

Systems and Control · Electrical Eng. & Systems 2025-02-04 Chaitanya Goswami , Pulkit Grover

Neonatal seizures are a commonly encountered neurological condition. They are the first clinical signs of a serious neurological disorder. Thus, rapid recognition and treatment are necessary to prevent serious fatalities. The use of…

Signal Processing · Electrical Eng. & Systems 2021-12-01 Vishal Nagarajan , Ashwini Muralidharan , Deekshitha Sriraman , Pravin Kumar S

Objective: A model is presented to evaluate the viability of using electrical impedance tomography (EIT) with a nerve cuff to record neural activity in peripheral nerves. Approach: Established modelling approaches in neural-EIT are expanded…

Neurons and Cognition · Quantitative Biology 2018-05-09 J. Hope , F. Vanholsbeeck , A. McDaid

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists…

Computer Vision and Pattern Recognition · Computer Science 2018-01-17 Ihsan Ullah , Muhammad Hussain , Emad-ul-Haq Qazi , Hatim Aboalsamh

Tinnitus is a prevalent auditory condition lacking objective biomarkers, motivating the search for reliable neural signatures. EEG, being a noninvasive method of brain imaging with a high temporal resolution provides a way to investigate…

Neurons and Cognition · Quantitative Biology 2026-04-27 Adyant Balaji , Abhinav Uppal , Min Suk Lee , Yuchen Xu , Akihiro Matsuoka , Gert Cauwenberghs

Electroencephalograph (EEG) analysis enables the neuronal behavior of a section of the brain to be examined. If the behavior is nonlinear then nonlinear tools can be used to glean information on brain behavior, and aid in the diagnosis of…

Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all…

Machine Learning · Computer Science 2023-01-18 Thi Kieu Khanh Ho , Narges Armanfard

Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions. Recently, several datasets have been made available that simultaneously record EEG activity and eye movements. This…

Signal Processing · Electrical Eng. & Systems 2023-08-14 Nina Weng , Martyna Plomecka , Manuel Kaufmann , Ard Kastrati , Roger Wattenhofer , Nicolas Langer

Objective Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for…

Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events.…

Signal Processing · Electrical Eng. & Systems 2022-02-17 Vahid Khalkhali , Nabila Shawki , Vinit Shah , Meysam Golmohammadi , Iyad Obeid , Joseph Picone

Epilepsy is the most common, chronic, neurological disease worldwide and is typically accompanied by reoccurring seizures. Neuro implants can be used for effective treatment by suppressing an upcoming seizure upon detection. Due to the…

Signal Processing · Electrical Eng. & Systems 2025-05-12 Julia Werner , Bhavya Kohli , Paul Palomero Bernardo , Christoph Gerum , Oliver Bringmann