Mental Workload Classification Method Based on EEG Independent Component Features

Qu, Hongquan; Shan, Yiping; Liu, Yuzhe; Pang, Liping; Fan, Zhanli; Zhang, Jie; Wanyan, Xiaoru

doi:10.3390/app10093036

Cited by 34 publications

(20 citation statements)

References 35 publications

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“…Moreover, while using the online P300 speller, the users can be tired and their mental workload may affect the classification as well. Thus, mental workload classification of EEG [48] is also planned to be used in future research. The possible usage of a spectrogram representation of the EEG signal is also considered being combined with ensemble learning in the future.…”

Section: Discussionmentioning

confidence: 99%

Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021

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Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.

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Section: Discussionmentioning

confidence: 99%

Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021

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“…For each epoch, Welch's method was used to extract the power spectral density (PSD), and the power of θ (3-8 Hz), α (8-13 Hz), β 1 (13)(14)(15)(16)(17)(18)(19)(20), and β 2 (20-30 Hz) were obtained. Then, seven-channel pairs (P8-P7, O2-O1, C2-C1, P4-P3, PZ-O1, PZ-O2, and O1-AF3) were selected from the left and right brain as well as the front and rear brain, and the energy difference of each channel pair in the θ, α, β 1 , and β 2 bands was calculated as the new feature.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Besides, there are still some researches on EEG for mental workload detection in complex tasks. Hongquan Qu et al [ 14 ] carried out a three-level Multi-Task Attribute Battery (MATB)[ 15 ] task with 32-channel electroencephalogram (EEG) acquisition. Power spectrum density (PSD) was analyzed with independent components analysis (ICA) algorithm, and the average recognition accuracy reached 79.8%.…”

Section: Introductionmentioning

confidence: 99%

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

et al. 2022

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Background Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application. Methods The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected. Results A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of $$\beta_{1}$$ β 1 and $$\beta_{2}$$ β 2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks. Conclusions The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

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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

“…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. Qu ve arkadaşları [16], EEG sinyallerine bağımsız bileşenler analizi uygulamış; bağımsız bileşenlerine ait güç spektral yoğunluklarını destek vektör makineleri ile sınıflandırmıştır. Bu çalışmada, zihinsel iş yükü seviyesi düşük, orta ve yüksek olmak üzere üç seviyeye sınıflandırılmasında %79.8 başarıya erişmişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

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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Mental Workload Classification Method Based on EEG Independent Component Features

Cited by 34 publications

References 35 publications

Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

Classification of Mental Workload Levels by Using EEG Signals

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