Across-subjects classification of stimulus modality from human MEG high frequency activity

Westner, Britta U.; Dalal, Sarang S.; Hanslmayr, Simon; Staudigl, Tobias

doi:10.1101/202424

Cited by 6 publications

(10 citation statements)

References 63 publications

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“…A substantial improvement in the performance of new strategy was observed compared with traditional ones. The predictive performance of one study on the SZ outcome of another was reported to be a key criterion for evaluating the reproducibility of the signatures identified by different datasets . The predictive performance (both ACCs and MCCs ) of all nine studies on the remaining eight independent datasets must be assessed to achieve the comprehensive assessment of the reproducibility of the method.…”

Section: Resultsmentioning

confidence: 99%

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Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility

Yang

Wang

et al. 2019

CNS Neurosci Ther

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Aims As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ. Methods In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted. Results Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ. Conclusion A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

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Section: Resultsmentioning

confidence: 99%

“…The performances of one study in predicting the SZ outcome of another and vice versa were critical criteria for assessing the reproducibility of the signatures identified from independent datasets . Thus, each of the nine independent datasets (Table ) was initially selected and used to identify SZ signature.…”

Section: Methodsmentioning

confidence: 99%

Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility

Yang

Wang

et al. 2019

CNS Neurosci Ther

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“…In order to use the DEAP dataset for the experimental evaluation of the model as well as the proposed method, a further pre-process dataset was needed. First, it was necessary to define a classification methodology for the stimuli stated by subjects [41]. We preferred binary classification, as has been suggested in [32].…”

Section: Deap Datasetmentioning

confidence: 99%

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Farooq¹,

Fiaz²,

Mehmood³

et al. 2021

Computers, Materials &Amp; Continua

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Game player modeling is a paradigm of computational models to exploit players' behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player's behavior within the game as well as the player's experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player's body or in the player's surroundings. In this paper, player experience modeling is studied based on the board puzzle game "Candy Crush Saga" using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players' effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players' engagement. Relevant not only from a player's point of view, it is also a benchmark study for game developers who have been facing problems of "cold start" for innovative games that strengthen the game industrial economy.

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“…The parameters were selected by performing informal parameter search. Random Forest was the algorithm of choice in our analysis due to its previous application to spectrotemporal features 87 and, more importantly, due to the interpretability of the resulting models. Highly interpretable structure of the resulting decision trees allowed us to follow the decision-making process that led each particular model to a decoding decision, and to estimate each feature's contribution to this process, allowing to identify and characterize category-specific spectral signatures.…”

Section: Random Forest As a Decoding Model A Random Forestmentioning

confidence: 99%

Identifying task-relevant spectral signatures of perceptual categorization in the human cortex

Kuzovkin

Vidal

Perrone‐Bertolotti

et al. 2020

Sci Rep

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Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks' selectivity for categorical information processing is still unknown. In this work we train Random Forest classification models to decode eight perceptual categories from broad spectrum of human intracranial signals (4-150 Hz, 100 subjects) obtained during a visual perception task. We then analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding and gain the insights into which parts of the recorded activity are actually characteristic of the visual categorization process in the human brain. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4-50 Hz) and high (50-150 Hz) frequency bands. By focusing on task-relevant neural activity and separating it into dissociated anatomical and spectrotemporal groups we uncover spectral signatures that characterize neural mechanisms of visual category perception in human brain that have not yet been reported in the literature.

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Across-subjects classification of stimulus modality from human MEG high frequency activity

Cited by 6 publications

References 63 publications

Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility

Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Identifying task-relevant spectral signatures of perceptual categorization in the human cortex

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