mdh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A Review on Machine Learning Algorithms in Handling EEG Artifacts
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-7305-7169
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-1212-7637
2014 (Engelska)Ingår i: The Swedish AI Society (SAIS) Workshop SAIS, 14, 2014Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

Ort, förlag, år, upplaga, sidor
2014.
Nyckelord [en]
Electroencephalograms (EEG), Artifacts, Machine Learning
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:mdh:diva-26427OAI: oai:DiVA.org:mdh-26427DiVA, id: diva2:759967
Konferens
The Swedish AI Society (SAIS) Workshop SAIS, 14, 22-23 May 2014, Stockholm, Sweden
Projekt
VDM - Vehicle Driver MonitoringTillgänglig från: 2014-11-01 Skapad: 2014-10-31 Senast uppdaterad: 2019-01-10Bibliografiskt granskad
Ingår i avhandling
1. Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
Öppna denna publikation i ny flik eller fönster >>Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
2015 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.

This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.

For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2015
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 217
Nyckelord
Artificial Intelligent, Intelligent systems, Physiological signal, Driver monitoring
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-28902 (URN)978-91-7485-231-8 (ISBN)
Presentation
2015-10-06, Lambda, Mälardalens högskola, Västerås, 13:15 (Engelska)
Opponent
Handledare
Projekt
Vehicle Driver Monitoring
Forskningsfinansiär
VINNOVA, VDM
Tillgänglig från: 2015-09-11 Skapad: 2015-09-10 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
2. Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
Öppna denna publikation i ny flik eller fönster >>Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.

This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.

Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.

This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2019
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 284
Nyckelord
Driver monitoring, Driver state, Physiological signals, Machine learning, Contextual information
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-42295 (URN)978-91-7485-419-0 (ISBN)
Disputation
2019-02-21, Zeta, Mälardalens högskola, Västerås, 13:15 (Engelska)
Opponent
Handledare
Projekt
VDM - Vehicle Driver Monitoring
Forskningsfinansiär
VINNOVA, VDM
Tillgänglig från: 2019-01-10 Skapad: 2019-01-10 Senast uppdaterad: 2019-01-23Bibliografiskt granskad

Open Access i DiVA

fulltext(901 kB)303 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 901 kBChecksumma SHA-512
68f97b5e4953cbeed0fbdcb1c51b22e340c07574d787580baa8bf1a4b4fef674203e936c81a7de4684f5ed7bb9dd6f9b41bce764796d4f1ff7ac8c18b5edb18d
Typ fulltextMimetyp application/pdf

Personposter BETA

Barua, ShaibalBegum, Shahina

Sök vidare i DiVA

Av författaren/redaktören
Barua, ShaibalBegum, Shahina
Av organisationen
Inbyggda system
Teknik och teknologier

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 303 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 27151 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf