TY - JOUR A1 - Kappattanavar, Arpita Mallikarjuna A1 - Hecker, Pascal A1 - Moontaha, Sidratul A1 - Steckhan, Nico A1 - Arnrich, Bert T1 - Food choices after cognitive load BT - an affective computing approach JF - Sensors N2 - Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications. KW - cognitive load KW - eating behaviour KW - machine learning KW - physiological signals KW - photoplethysmography KW - electrodermal activity KW - sensors Y1 - 2023 U6 - https://doi.org/10.3390/s23146597 SN - 1424-8220 VL - 23 IS - 14 PB - MDPI CY - Basel ER - TY - JOUR A1 - Moontaha, Sidratul A1 - Schumann, Franziska Elisabeth Friederike A1 - Arnrich, Bert T1 - Online learning for wearable EEG-Based emotion classification JF - Sensors N2 - Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9% (Arousal) and 25.8% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87% (Arousal) and 82% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification. KW - online learning KW - real-time KW - emotion classification KW - AMIGOS dataset KW - wearable EEG (muse and neurosity crown) KW - psychopy experiments Y1 - 2023 U6 - https://doi.org/10.3390/s23052387 SN - 1424-8220 VL - 23 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Galka, Andreas A1 - Moontaha, Sidratul A1 - Siniatchkin, Michael T1 - Constrained expectation maximisation algorithm for estimating ARMA models in state space representation JF - EURASIP journal on advances in signal processing N2 - This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from linear mixtures, and partly from the need to keep models identifiable. The first stage of parameter fitting is performed by the expectation maximisation (EM) algorithm. Due to the identifiability constraint, a subset of the diagonal elements of the dynamical noise covariance matrix needs to be constrained to fixed values (usually unity). For this kind of constraints, so far, no closed-form update rules were available. We present new update rules for this situation, both for updating the dynamical noise covariance matrix directly and for updating a matrix square-root of this matrix. The practical applicability of the proposed algorithm is demonstrated by a low-dimensional simulation example. The behaviour of the EM algorithm, as observed in this example, illustrates the well-known fact that in practical applications, the EM algorithm should be combined with a different algorithm for numerical optimisation, such as a quasi-Newton algorithm. KW - Kalman filtering KW - state space modelling KW - expectation maximisation algorithm Y1 - 2020 U6 - https://doi.org/10.1186/s13634-020-00678-3 SN - 1687-6180 VL - 2020 IS - 1 PB - Springer CY - Heidelberg ER -