This month, many key documents and papers were read and reviewed. These articles included both practical and theory based research papers, explanatory books, and other literature reviews. These documents provided a solid foundation for the project to be planned from, and confirmed the investigator’s interest in using machine learning for emotion classification. See bibliography list attached.
Aggarwal, C., 2018. Neural Networks and Deep Learning. Springer International Publishing.
Altenmüller, E., 2002. Hits to the left, flops to the right: different emotions during listening to music are reflected in cortical lateralisation patterns. Neuropsychologia, 40(13), pp.2242-2256.
Bird, J., Manso, L., Ribeiro, E., Ekart, A. and Faria, D., 2018. A Study on Mental State Classification using EEG-based Brain-Machine Interface. 2018 International Conference on Intelligent Systems (IS), pp.795-800.
Kaplan, S., Dalal, R. and Lunchman, J., 2013. Measurement of emotions. In: L. Tetrick, M. Wang and R. Sinclair, ed., Research Methods in Occupational Health Psychology, 2nd ed. Routledge, pp.61-75.
Kappeler, K., 2010. Extraction of valence and arousal information from EEG signals for emotion classification. Master Dissertation. Swiss Federal Institute of Technology in Lausanne.
Mauss, I. and Robinson, M., 2009. Measures of emotion: A review. Cognition Emotion, 23(2), pp.209-237.
Posner, J., Russell, J. and Peterson, B., 2005. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17(03).
Savran, A., Ciftci, K., Chanel, G., Mota, J., Viet, L., Sankur, B., Akarun, L., Caplier, A. and Rombaut, M., 2006. Emotion Detection in the Loop from Brain Signals and Facial Images. [online] Dubrovnik