ArticleSensors (Basel, Switzerland)2016
Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Consecutive Image Acquisition without Anomalies.Sensors (Basel, Switzerland) · 2024Article
- A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.Sensors (Basel, Switzerland) · 2024Article
- A Neural Algorithm for the Detection and Correction of Anomalies: Application to the Landing of an Airplane.Sensors (Basel, Switzerland) · 2022Article
- Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification.Sensors (Basel, Switzerland) · 2020Article
- An Unsupervised Method for Artefact Removal in EEG Signals.Sensors (Basel, Switzerland) · 2019Article
- Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2018Article
- Towards Building a Computer Aided Education System for Special Students Using Wearable Sensor Technologies.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.