Learning mental states from biosignal
Başlık çevirisi mevcut değil.
- Tez No: 402987
- Danışmanlar: PROF. SAMUEL KASKI, DR. ARTO KLAMI
- Tez Türü: Doktora
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2013
- Dil: İngilizce
- Üniversite: Aalto Unıversıty (aalto Ylıopısto)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Özet yok.
Özet (Çeviri)
As computing technology evolves, users perform more complex tasks with computers. Hence, users expect from user interfaces to be more proactive than reactive. A proactive interface should anticipate the user's intentions and take the right action without requiring a user command. The crucial first step for such an interface is to infer the user's mental state, which gives important cues about user intentions. This thesis consists of several case studies on inferring mental states of computer users. Biosensing technology provides a variety of hardware tools for measuring several aspects of human physiology, which is correlated with emotions and mental processes. However, signals gathered with biosensors are notoriously noisy. The mainstream approach to overcome this noise is either to increase the signal precision by expensive and stationary sensors or to control the experiment setups more heavily. Both of these solutions undermine the usability of the developed methods in real-life user interfaces. In this thesis, machine learning is used as an alternative strategy for handling the biosignal noise in mental state inference. Computer users have been monitored under loosely controlled experiment setups by cheap and inaccurate biosensors, and novel machine learning models that infer mental states such as affective state, mental workload, relevance of a real-world object, and auditory attention are built. The methodological contributions of the thesis are mainly on multi-view learning and multitask learning. Multi-view learning is used for integrating signals of multiple biosensors and the stimuli. Multitask learning is used for inferring multiple mental states at once, and for exploiting the inter-subject similarities for higher prediction accuracy. A novel multitask learning algorithm that transfers knowledge across multi-view learning tasks is introduced. Another novelty is a Bayesian factor analyzer with a time-dependent latent space that captures the dynamic nature of biosignals better than methods that assume independent samples. The overall outcome of the thesis is that it is feasible to predict mental states from unobtrusive biosensors with reasonable accuracy using state-of-the-art machine learning models. Keywords Multitask Learning, Multiple Kernel Learning, Probabilistic Modeling, Affective Computing, Intelligent User Interfaces
Benzer Tezler
- Obtaining EEG-based features of mental states with brain-computer interfaces using machine learning
Makine öğrenmeyi kullanarak beyin-bilgisayar arayüzleri ile zihinsel durumların EEG tabanlı özelliklerinin elde edilmesi
AHSAN MUMTAZ
Yüksek Lisans
İngilizce
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolKarabük ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
YRD. DOÇ. DR. IMAN ELAWADY
- Bilişsel duygu durumlarının iş performansına etkisinin makine öğrenmesi ile araştırılması
Investigation of the effect of cognitive emotional states on task performance using machine learning
DERYA DALFİDAN
Doktora
Türkçe
2026
Endüstri ve Endüstri MühendisliğiBursa Uludağ ÜniversitesiEndüstri Mühendisliği Ana Bilim Dalı
PROF. DR. TÜLİN GÜNDÜZ
- Multi-task learning on mental disorder detection, sentiment detection, and emotion detection
Zihinsel bozukluk tespiti, duygusallık(sentiment) tespiti ve duygu tespiti üzerinde çok görevli öğrenim
COURAGE ARMAH
Yüksek Lisans
İngilizce
2024
PsikolojiIşık ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ RAHIM DEHKHARGHANI
- EEG verileriyle zihinsel kapasitenin açıklanabilir makine öğrenmesi tabanlı modellenmesi
Explainable machine learning-based modeling of mental performance using EEG data
UĞUR İNCE
Doktora
Türkçe
2026
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolFırat ÜniversitesiAdli Bilişim Mühendisliği Ana Bilim Dalı
PROF. DR. TÜRKER TUNCER
- En az 2 yıldır remisyonda olan lösemi ve lenfoma tanısı almış çocuk ve gençlerin ruhsal durumları, bilişsel fonksiyonları, yaşam kalitesi ve bunları etkileyen değişkenlerin belirlenmesi
Assessment of mental states, cognitive functions, life quality and related variables of children and adolescents under remission for at least two years with leukemia and lymphoma diagnosis
MUSTAFA KÜÇÜKKÖSE
Tıpta Uzmanlık
Türkçe
2010
Çocuk Sağlığı ve HastalıklarıEge ÜniversitesiÇocuk Ruh Sağlığı ve Hastalıkları Ana Bilim Dalı
DOÇ. DR. SERPİL ERERMİŞ