@article{MoontahaSchumannArnrich2023,
  author    = {Moontaha, Sidratul and Schumann, Franziska Elisabeth Friederike and Arnrich, Bert},
  title     = {Online learning for wearable EEG-Based emotion classification},
  series = {Sensors},
  volume    = {23},
  journal   = {Sensors},
  number    = {5},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s23052387},
  pages     = {23},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{GalkaMoontahaSiniatchkin2020,
  author    = {Galka, Andreas and Moontaha, Sidratul and Siniatchkin, Michael},
  title     = {Constrained expectation maximisation algorithm for estimating ARMA models in state space representation},
  series = {EURASIP journal on advances in signal processing},
  volume    = {2020},
  journal   = {EURASIP journal on advances in signal processing},
  number    = {1},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1687-6180},
  doi       = {10.1186/s13634-020-00678-3},
  pages     = {37},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}