EEG Based Stress Monitoring

Hou, Xiyuan; Liu, Yisi; Sourina, Olga; Tan, Yun; Wang, Lipo; Müeller-Wittig, Wolfgang

doi:10.1109/smc.2015.540

Cited by 162 publications

(67 citation statements)

References 31 publications

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“…Sinyal EEG inilah yang menjadi sumber informasi untuk mempelajari fungsi dan keadaan otak manusia, sehingga banyak digunakan. Beberapa penelitian telah dilakukan untuk mengetahui aktivitas otak yang erat kaitannya dengan kondisi stres maupun rileks [6]- [8]. Kondisi nyaman dalam sebuah ruangan sangat mendukung sekali dalam meningkatkan produktivitas seseorang dan kesehatan.…”

Section: Pendahuluanunclassified

Support Vector Classifier Pada Tingkat Kenyamanan Termal Dalam Kondisi Rileks Terhadap Perubahan Suhu Ruangan Berbasis Sinyal Eeg

Saputra

Ratri

2019

Seminar Nasional Kontrol, Instrumentasi Dan Otomasi (SNIKO) 2018

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AbstrakKontrol suhu ruangan yang ada telah dikembangkan saat ini hanya memanfaatkan keadaan lingkungan/ruangan sebagai parameter untuk mengontrol suhu ruangan. Model kontrol seperti ini mengabaikan kondisi fisologis dan psikologi seseorang yang sedang berada di dalam ruangan. Setiap orang memiliki tingkat kenyamanan yang berbeda-beda saat berada di dalam ruangan berpendingin. Kondisi nyaman tersebut dipengeruhi oleh termoregulasi seseorang untuk memenuhi kenyamanan termal. Sinyal EEG merupakan sinyal yang mampu merefleksikan aktivitas otak. Karena kenyamanan termal erat kaitannya dengan kondisi kepuasan pikirian atas kondisi termal lingkungan, maka sinyal EEG ini dirasa mampu merefleksikan tingkat kenyamanan termal seseorang. Penelitian ini akan menganalisa tingkat kenyamanan termal seseorang berbasis sinyal EEG. Sinyal EEG direkam menggunakan alat mindwave neurosky headset. Sinyal EEG direkam dalam kondisi rileks. Hasil data sinyal yang didapat akan dianalisis dan dilatihkan pada classifier SVC. Pengujian dilakukan dengan menggunakan metode k-fold validation dengan menggunakan nilai k = 5. Analisis lebih lanjut terkait performa keseluruhan dari classifier tersebut menggunakan kurva ROC. Hasil yang didapat dengan analisis kurva ROC ialah performa classifier SVC sebesar 87%.Kata Kunci: support vector classifier, sinyal eeg, kenyamanan termal.

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Section: Pendahuluanunclassified

Support Vector Classifier Pada Tingkat Kenyamanan Termal Dalam Kondisi Rileks Terhadap Perubahan Suhu Ruangan Berbasis Sinyal Eeg

Saputra

Ratri

2019

Seminar Nasional Kontrol, Instrumentasi Dan Otomasi (SNIKO) 2018

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“…As mentioned by [11], [12], STW is a reliable protocol to cause stress to participant in laboratory situation. The experiment protocol for this study was designed based on [12]- [14] and developed using MATLAB. Fig.…”

Section: Experimental Protocolmentioning

confidence: 99%

Emotional Stroop Task Recognition by Band-Localize Fractal Dimension Method

Hamid¹,

Saad²,

Haris³

2019

IJITEE

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This study aims to investigate the association between bipolar dimension emotional model (based on band-localize fractal dimension (BLFD) feature) and Stroop colour-word test (STW). Twelve healthy participants are voluntarily took part in this study. Participants’ emotion during STW test was measured using a wireless electroencephalogram (EEG) sensor. Three STW sessions were introduced in this study based on different stress level induced. Alpha and beta frequency band were extracted from EEG signals using six-level wavelet decomposition. Then, BLFD features was computed based on Higuchi method. Lastly, valence and arousal levels were calculated and mapped to bipolar dimension model in estimating participant emotional states. Results showed that valence level was decreased at beginning of each session and increase towards the end of the session. On the other hand, decreasing trend was observed for arousal level at each session.

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“…Besides these, parietal and motor cortex regions are found to take significantly active participation during the experiment. Additionally, literature [1][2][3][4][5][6] reveals that temporal lobe is highly associated with human speech signal processing. Therefore, we select P3, P4 and Pz (from parietal lobe), T1, T2, T3, T4, T5 and T6 (from temporal lobe) and C3, C4 and Cz (from motor cortex region) for extracting necessary information by applying signal processing techniques.…”

Section: B Experiments 1: Selection Of Brain Regionsmentioning

confidence: 99%

“…There are so many research work based on real time EEG-based human emotion recognition [2], [3] and EEG based stress monitoring is described in paper [4]. In paper [5], one channel of ICA data is taken as input which increases classification accuracy to 87%.…”

Section: Introductionmentioning

confidence: 99%

EEG-based mind driven type writer by fuzzy radial basis function neural classifier

Lall

Saha

Konar

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-EEG based vowel classification is currently gaining importance for its increasing applications in the next generation mind-driven type-writing. This paper addresses a novel approach to classify the mentally uttered alphabets in a specific three lettered format, where the first and the last letter represent two vowel sounds and the middle is a space, where no character is imagined. Such formatting helps recognizing 26 alphabets in English language using seven vowel sounds only. To eliminate the possible infiltration of noise by parallel thoughts we used a specialized neuro-fuzzy classifier, where the first layer of the classifiers realized with fuzzy logic eliminates the possible creeping of noise due to side active channel interference. Two models of fuzzy preprocessing are used. The first one is realized with type-1 fuzzy logic, whereas the second model is realized with interval type-2 fuzzy sets. The latter model can take care of both intra-and inter-personal level uncertainty in measurements. Experiments undertaken reveal that the proposed type-2 fuzzy classifier outperforms both type-1 and traditional neural classifiers by a significant margin.

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EEG Based Stress Monitoring

Cited by 162 publications

References 31 publications

Support Vector Classifier Pada Tingkat Kenyamanan Termal Dalam Kondisi Rileks Terhadap Perubahan Suhu Ruangan Berbasis Sinyal Eeg

Support Vector Classifier Pada Tingkat Kenyamanan Termal Dalam Kondisi Rileks Terhadap Perubahan Suhu Ruangan Berbasis Sinyal Eeg

Emotional Stroop Task Recognition by Band-Localize Fractal Dimension Method

EEG-based mind driven type writer by fuzzy radial basis function neural classifier

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