EEG-Based Classification of the Driver Alertness State
Abstract: GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
EEG-Based Classification of the Driver Alertness State ; volume:6 ; number:3 ; year:2020 ; pages:353-356 ; extent:4
Current directions in biomedical engineering ; 6, Heft 3 (2020), 353-356 (gesamt 4)
- Urheber
-
Golz, Martin
Thomas, Sebastian
Schenka, Adolf
- DOI
-
10.1515/cdbme-2020-3091
- URN
-
urn:nbn:de:101:1-2022101215264616994698
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:33 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Golz, Martin
- Thomas, Sebastian
- Schenka, Adolf
Ähnliche Objekte (12)
Power spectral density-based resting-state EEG classification of first-episode psychosis
EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm
Resting State EEG Classification for Motor Learning Skills Using Echo State Networks
EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)
Statistical Machine Learning in Brain State Classification using EEG Data
Review of EEG-based pattern classification frameworks for dyslexia
Machine Learning Supervised Classification Methodology for Autism Spectrum Disorder based on Resting-State Electroencephalography (EEG) Signals
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
EEG-based trial-by-trial texture classification during active touch
Two-stepped majority voting for efficient EEG-based emotion classification
Drivers’ EEG Responses to Different Distraction Tasks
CTNet: a convolutional transformer network for EEG-based motor imagery classification
Power spectral density-based resting-state EEG classification of first-episode psychosis
EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm
Resting State EEG Classification for Motor Learning Skills Using Echo State Networks
EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)
Statistical Machine Learning in Brain State Classification using EEG Data
Review of EEG-based pattern classification frameworks for dyslexia
Machine Learning Supervised Classification Methodology for Autism Spectrum Disorder based on Resting-State Electroencephalography (EEG) Signals
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
EEG-based trial-by-trial texture classification during active touch
Two-stepped majority voting for efficient EEG-based emotion classification
Drivers’ EEG Responses to Different Distraction Tasks
CTNet: a convolutional transformer network for EEG-based motor imagery classification
Power spectral density-based resting-state EEG classification of first-episode psychosis
EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm
Resting State EEG Classification for Motor Learning Skills Using Echo State Networks
EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)
Statistical Machine Learning in Brain State Classification using EEG Data
Review of EEG-based pattern classification frameworks for dyslexia
Machine Learning Supervised Classification Methodology for Autism Spectrum Disorder based on Resting-State Electroencephalography (EEG) Signals
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
EEG-based trial-by-trial texture classification during active touch
Two-stepped majority voting for efficient EEG-based emotion classification
Drivers’ EEG Responses to Different Distraction Tasks