Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Assessing NeuroSky's Usability to Detect Attention Levels in an Assessment Exercise
Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends
Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems
Microelectronics Journal
A decision theoretic model for stress recognition and user assistance
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On classifiability of wavelet features for EEG-based brain-computer interfaces
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Emotion recognition from EEG using higher order crossings
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Affective, natural interaction using EEG: sensors, application and future directions
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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We introduce a system called AMBER (Advanced Multimodal Biometric Emotion Recognition), which combines Electroencephalography (EEG) with Electro Dermal Activity (EDA) and pulse sensors to provide low cost, portable real-time emotion recognition. A single-subject pilot experiment was carried out to evaluate the ability of the system to distinguish between positive and negative states of mind provoked by audio stimuli. Eight single classifiers and six ensemble classifiers were compared using Weka. All ensemble classifiers outperformed the single classifiers, with Bagging, Rotation Forest and Random Subspace showing the highest overall accuracy.