STEW: Simultaneous Task EEG Workload Data Set

Lim, Wei Lun; Sourina, Olga; Wang, L. P.

doi:10.1109/tnsre.2018.2872924

Cited by 124 publications

(106 citation statements)

References 35 publications

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“…For instance, mental workload increases theta power and reduce alpha power activity (Mun et al, 2017), whereas fatigue increases alpha power (Käthner et al, 2014). Moreover, the development of novel computational techniques for analyzing spectral activity has promoted a wide range of new tools for probing ongoing neural dynamics during human cognition via EEG; such as cross-frequency coupling, phase coupling (Cohen, 2011), independent component analysis (Dasari et al, 2017), and neighborhood component analysis (Lim et al, 2018). In addition, more traditional analyses of transient neural activity that is tied to specific perceptual, motor, or cognitive events can be gleaned from continuous EEG, via the calculation of event-related brain potentials.…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

230

192

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

230

192

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“…Two different sets of features have been considered. In the first case, the PSD values in the theta and alpha bands have been computed for 14 EEG electrodes, to imitate what is usually done in several studies in literature [9], [41], [43], [53]. In this work, 28 PSD Features (14 Channels x 2 Bands) have been computed.…”

Section: Features Extractionmentioning

confidence: 99%

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Sciaraffa

Aricò

Borghini

et al. 2019

Communications in Computer and Information Science

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The measurement of the mental workload during real tasks by means of neurophysiological signals is still challenging. The employment of Machine Learning techniques has allowed a step forward in this direction, however, most of the work has dealt with binary classification. This study proposed to examine the surveys already performed in the context of EEG-based workload classification and to test different machine learning algorithms on real multitasking activity like the Air Traffic Management. The results obtained on 35 professional Air Traffic Controllers showed that a KNN algorithm allows discriminating up to three workload levels (low, medium and high) with more than 84 % of accuracy on average. Moreover, in such realistic employment it emerges how important is to opportunely choose the set of features to ward off that task-related confounds could affect the workload assessment.

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“…Beyindeki bilişsel aktiviteler sonucu oluşan değişimleri algılama hassasiyetinin yüksek olması nedeniyle, EEG sinyalleri zihinsel iş yükü değerlendirmelerinde sıklıkla kullanılan bir yöntemdir [13][14]. Zihinsel iş yükü seviyelerinin çok seviyeli sınıflandırılması üzerine çeşitli çalışmalar yürütülmüştür [15][16][17][18][19]. Wang ve arkadaşları [15], dört spesifik EEG alt bandı için hesapladıkları, entropi, sinyal gücü, morfolojik ve istatistiksel öznitelikleri içeren 658 öznitelikten oluşan öznitelik vektörünü kullanarak, mRMR tabanlı öznitelik seçimi ve destek vektör makineleri tabanlı sınıflandırma sonucu %84 sınıflandırma doğruluğuna erişmişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

“…Chin ve arkadaşları [17], EEG sinyal bant güçleri ve doğrusal ayırtaç analizi yöntemleri ile, mental aritmetik görevleri sırasındaki zihinsel iş yükünü kolay, orta ve zor olmak üzere %66 doğrulukla sınıflandırmıştır. Lim ve arkadaşları [18], 48 katılımcının kendilerine verilen çoklu görevleri gerçekleştirmeleri sırasında kaydedilen EEG sinyallerini içeren bir zihinsel iş yükü aktivitesi veri kümesi oluşturmuşlardır. Hızlı Fourier Dönüşümü ile alt bantlarına ayrıştırılmış EEG sinyallerinin her alt bant için güç spektral yoğunluklarını öznitelik olarak kullanarak, düşük, orta ve yüksek zihinsel iş yükünü %69 doğrulukla belirlemişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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Classification of Mental Workload Levels by Using EEG Signals

Aydın

2021

Politeknik Dergisi

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 Fraktal boyut algoritmaları zihinsel iş yükü seviyelerinin sınıflandırılmasında kullanılabilir./Fractal dimension algorithms can be used for classifying mental workload levels.  Higuchi fraktal boyut (HFB) algoritması destek vektör makineleri ile birlikte zihinsel iş yükü seviyelerinin sınıflandırılmasında en yüksek sınıflandırma doğruluğu sağlamıştır./Higuchi fractal dimension (HFB) algorithm combined with support vector machines provided the highest classification accuracy in classifying mental workload levels.

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STEW: Simultaneous Task EEG Workload Data Set

Cited by 124 publications

References 35 publications

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Classification of Mental Workload Levels by Using EEG Signals

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