Geri Dön

Learning mental states from biosignal

Başlık çevirisi mevcut değil.

  1. Tez No: 402987
  2. Yazar: MELİH KANDEMİR
  3. Danışmanlar: PROF. SAMUEL KASKI, DR. ARTO KLAMI
  4. Tez Türü: Doktora
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2013
  8. Dil: İngilizce
  9. Üniversite: Aalto University (Aalto Yliopisto)
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 98

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

  1. 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

    İngilizce

    2024

    Bilim ve TeknolojiIşık Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    DR. ÖĞR. ÜYESİ EMİNE EKİN

  2. Makine öğrenmesi yöntemleri kullanılarak EEG verisinden zihinsel dikkat durumu tespiti

    Determination of mental attention state with EEG based bci usingmachine learning methods

    MURAT KAYA

    Yüksek Lisans

    Türkçe

    Türkçe

    2019

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolMersin Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    DR. ÖĞR. ÜYESİ ÇİĞDEM ACI

    DOÇ. DR. YURİY MİSHCHENKO

  3. Sorgulamaya dayalı öğrenmenin üstün zekalı ve yetenekli öğrencilerin asitler-bazlar konusunu anlamalarına ve fen öğrenimine yönelik motivasyonlarına etkisi

    The effect of inquiry based learning on gifted and talented students' understanding of acids-bases concepts and motivation towards science learning

    SİNEM DİNÇOL ÖZGÜR

    Doktora

    Türkçe

    Türkçe

    2016

    Eğitim ve ÖğretimHacettepe Üniversitesi

    Ortaöğretim Fen ve Matematik Öğretmenliği Eğ. Ana Bilim Dalı

    PROF. DR. AYHAN YILMAZ

  4. 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

    İngilizce

    2024

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolKarabük Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    YRD. DOÇ. DR. IMAN ELAWADY

  5. 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

    Türkçe

    2010

    Çocuk Sağlığı ve HastalıklarıEge Üniversitesi

    Çocuk Ruh Sağlığı ve Hastalıkları Ana Bilim Dalı

    DOÇ. DR. SERPİL ERERMİŞ