Relevant bibliographies by topics / Speech prediction EEG

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Speech prediction EEG'

Author: Grafiati
Published: 14 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "Speech prediction EEG"

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Anderson, Andrew J., Chris Davis, and Edmund C. Lalor. "Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation." PLOS Computational Biology 20, no. 11 (2024): e1012537. http://dx.doi.org/10.1371/journal.pcbi.1012537.

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To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may ove
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Maki, Hayato, Sakriani Sakti, Hiroki Tanaka, and Satoshi Nakamura. "Quality prediction of synthesized speech based on tensor structured EEG signals." PLOS ONE 13, no. 6 (2018): e0193521. http://dx.doi.org/10.1371/journal.pone.0193521.

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Ishii, Chikara, Hiroki Watanabe, Yasushi Naruse, and Aya S. Ihara. "Prediction of mutual satisfaction in natural conversation using EEG and speech behavior." Proceedings of the Annual Convention of the Japanese Psychological Association 88 (2024): 1C—059—PG—1C—059—PG. https://doi.org/10.4992/pacjpa.88.0_1c-059-pg.

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Wikman, Patrik, Viljami Salmela, Eetu Sjöblom, Miika Leminen, Matti Laine, and Kimmo Alho. "Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network." PLOS Biology 22, no. 3 (2024): e3002534. http://dx.doi.org/10.1371/journal.pbio.3002534.

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Selective attention-related top-down modulation plays a significant role in separating relevant speech from irrelevant background speech when vocal attributes separating concurrent speakers are small and continuously evolving. Electrophysiological studies have shown that such top-down modulation enhances neural tracking of attended speech. Yet, the specific cortical regions involved remain unclear due to the limited spatial resolution of most electrophysiological techniques. To overcome such limitations, we collected both electroencephalography (EEG) (high temporal resolution) and functional m
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Rogachev, A. O., and O. V. Sysoeva. "Neural tracking of natural speech listening in children: temporal response function (TRF) approach." Genes & Cells 18, no. 4 (2023): 640–44. http://dx.doi.org/10.17816/gc623394.

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Speech development is crucial for a child’s mental growth. Moreover, speech development significantly impacts a child’s educational and professional achievements. It enables the child to interact with the external environment and develop self-awareness and behavioral skills. Thus, the study of the mechanisms of speech development disorders and the development of diagnostic and remediation strategies is essential. Numerous cognitive and neurophysiological investigations into speech and its associated disorders among children are presently being conducted. Electroencephalography (EEG) studies de
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Gibson, Jerry. "Entropy Power, Autoregressive Models, and Mutual Information." Entropy 20, no. 10 (2018): 750. http://dx.doi.org/10.3390/e20100750.

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Autoregressive processes play a major role in speech processing (linear prediction), seismic signal processing, biological signal processing, and many other applications. We consider the quantity defined by Shannon in 1948, the entropy rate power, and show that the log ratio of entropy powers equals the difference in the differential entropy of the two processes. Furthermore, we use the log ratio of entropy powers to analyze the change in mutual information as the model order is increased for autoregressive processes. We examine when we can substitute the minimum mean squared prediction error
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Sohoglu, Ediz, and Matthew H. Davis. "Perceptual learning of degraded speech by minimizing prediction error." Proceedings of the National Academy of Sciences 113, no. 12 (2016): E1747—E1756. http://dx.doi.org/10.1073/pnas.1523266113.

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Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent ma
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Da, Silva Souto Carlos F., Wiebke Pätzold, Marina Paul, Stefan Debener, and Karen Insa Wolf. "Pre-gelled Electrode Grid for Self-Applied EEG Sleep Monitoring at Home." Frontiers in Neuroscience 16 (June 5, 2022): 1–11. https://doi.org/10.3389/fnins.2022.883966.

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Shen, Stanley, Jess R. Kerlin, Heather Bortfeld, and Antoine J. Shahin. "The Cross-Modal Suppressive Role of Visual Context on Speech Intelligibility: An ERP Study." Brain Sciences 10, no. 11 (2020): 810. http://dx.doi.org/10.3390/brainsci10110810.

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The efficacy of audiovisual (AV) integration is reflected in the degree of cross-modal suppression of the auditory event-related potentials (ERPs, P1-N1-P2), while stronger semantic encoding is reflected in enhanced late ERP negativities (e.g., N450). We hypothesized that increasing visual stimulus reliability should lead to more robust AV-integration and enhanced semantic prediction, reflected in suppression of auditory ERPs and enhanced N450, respectively. EEG was acquired while individuals watched and listened to clear and blurred videos of a speaker uttering intact or highly-intelligible d
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Teixeira, Felipe Lage, Miguel Rocha e. Costa, José Pio Abreu, Manuel Cabral, Salviano Pinto Soares, and João Paulo Teixeira. "A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges." Bioengineering 10, no. 4 (2023): 493. http://dx.doi.org/10.3390/bioengineering10040493.

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Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate pos
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Dissertations / Theses on the topic "Speech prediction EEG"

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Vidal, Dos Santos Hector Yamil. "Phonological prediction in speech processing." Doctoral thesis, SISSA, 2016. http://hdl.handle.net/20.500.11767/4927.

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Auditory speech perception can be described as the task of mapping an auditory signal into meaning. We routinely perform this task in an automatic and effortless manner, which might conceal the complexity behind this process. It should be noted that the speech signal is highly variable, ambiguous and usually perceived in noise. One possible strategy the brain might use to handle this task is to generate predictions about the incoming auditory stream. Prediction occupies a prominent role in cognitive functions ranging from perception to motor control. In the specific case of speech perce
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Cheimariou, Spyridoula. "Prediction in aging language processing." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/3056.

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This thesis explores how predictions about upcoming linguistic stimuli are generated during real-time language comprehension in younger and older adults. Previous research has shown humans' ability to use rich contextual information to compute linguistic prediction during real-time language comprehension. Research in the modulating factors of prediction has shown, first, that predictions are informed by our experience with language and second, that these predictions are modulated by cognitive factors such as working memory and processing speed. However, little is known about how these factors
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Richieri, Raphaëlle. "Substrats neuro-fonctionnels de la stimulation magnétique transcrânienne répétitive dans la dépression pharmaco-résistante." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5026.

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La pharmaco-résistance est une complication évolutive fréquente de l'épisode dépressif majeur. La rTMS est une technique de stimulation cérébrale innovante dont l'efficacité antidépressive est maintenant établie.Le premier objectif de notre travail de thèse a été de caractériser les substrats fonctionnels de la dépression pharmaco-résistante à l'aide de la technique TEMP, afin d'identifier des patterns d'anomalies cérébrales qui leur sont propres. Dans un second temps, et sur la base des travaux existant sur les mécanismes d'action de la rTMS, nous avons étudié la valeur prédictive de marqueur
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Book chapters on the topic "Speech prediction EEG"

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Gallego-Molina, N., F. J. Martinez-Murcia, M. A. Formoso, et al. "A Survey on EEG Phase Amplitude Coupling to Speech Rhythm for the Prediction of Dyslexia." In Artificial Intelligence for Neuroscience and Emotional Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61140-7_16.

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Varra, Rachel. "Sociolinguistic dimensions of flagging behavior." In Issues in Hispanic and Lusophone Linguistics. John Benjamins Publishing Company, 2025. https://doi.org/10.1075/ihll.42.02var.

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Abstract Pauses, fillers (eh, um) and false starts (e.g. recu — recuperación) are often interpreted as ‘disfluency’ in monolingual and bilingual speech. Recent research on these elements (here called ‘flags’) increasingly recognizes their communicative functions, but few studies examine how sociolinguistic factors contribute to their use. This study investigates how social factors interact with flagging among Spanish-English bilinguals. Silent pauses, fillers and false starts in the monolingual discourse of 115 bilinguals are examined and results indicate that ethnonational affiliation is the main predictor of flagging. Results suggest that flagging elements may function as sociolinguistic indices for bilinguals and that bilinguals use flagging elements differently than in bilingual mixed speech and differently than monolinguals. Methodological implications and limitations of the study are discussed.
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Staubach, J., and S. Stephan. "Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models." In Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35779-4_10.

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AbstractFor machining processes, such as drilling, grinding, and cutting, fluids play a crucial role for lubrication and cooling. For adequately describing such processes, robust models for the thermophysical properties of the fluids are a prerequisite. In the contact zone, extreme conditions prevail, e.g. regarding temperature and pressure. As thermophysical property data at such conditions are presently often not available, predictive and physical models are required. Molecular-based equations of state (EOS) are attractive candidates as they provide a favorable trade-off between computational speed and predictive capabilities. Yet, without experimental data, it is not trivial to assess the physical reliability of a given EOS model. In this work, Brown’s characteristic curves are used to assess molecular-based fluid models. Brown’s characteristic curves provide general limits that are to be satisfied such that a given model is thermodynamically consistent. Moreover, a novel approach was developed, which uses pseudo-experimental data obtained from molecular simulations using high-accurate force fields. The method is generalized in a way that it can be applied to different force field types, e.g. model potentials and complex real substances. The method was validated based on the (scarcely) available data in the literature. Based on this pseudo-experimental data, different thermodynamic EOS models were assessed. Only the SAFT-VR Mie EOS is found to yield thermodynamically consistent results in all cases. Thereby, robust EOS models were identified that can be used for reliably modeling cutting fluids at extreme conditions, e.g. in machining processes.
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Di Napoli, Claudia, Alessandro Messeri, Martin Novák, et al. "The Universal Thermal Climate Index as an Operational Forecasting Tool of Human Biometeorological Conditions in Europe." In Applications of the Universal Thermal Climate Index UTCI in Biometeorology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76716-7_10.

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AbstractIn operational weather forecasting standard environmental parameters, such as air temperature and humidity, are traditionally used to predict thermal conditions in the future. These parameters, however, are not enough to describe the thermal stress induced by the outdoor environment to the human body as they neglect the human heat budget and personal characteristics (e.g. clothing). The Universal Thermal Climate Index (UTCI) overcomes these limitations by using an advanced thermo-physiological model coupled with a state-of-the-art clothing model. Several systems have been recently developed to operationally forecast human biometeorological conditions via the UTCI, i.e. by computing UTCI from the forecasts of air temperature, humidity, wind speed and radiation as provided by numerical weather prediction models. Here we describe the UTCI-based forecasting systems developed in Czech Republic, Italy, Poland, Portugal and at the pan-European scale. Their characteristics are illustrated and their potential as warning systems for thermal hazards discussed.
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Bähr, Philipp, Lukas Leidinger, Silke Sommer, and Stefan Hartmann. "Validation of the *CONSTRAINED_SPR3 Joint Formulation for Isogeometric Shell Models." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_2.

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Abstract Isogeometric Analysis (IGA) uses higher-order and higher-continuity spline basis functions known from Computer Aided Design (CAD) to describe the geometry and the solution field of the simulation model (mainly Non-Uniform Rational B-splines). This leads to a more accurate geometry description, a smooth solution field and therefore superior simulation properties compared to traditional Finite Element Analysis (FEA). Using the same geometry description for CAD and IGA also speeds up the modeling process for the simulation. Real components consist of multiple sheet metal parts connected by point-like joints (spot-welds, rivets, screws). These joints significantly influence the component behaviour under crash load. Therefore, their properties must be accurately described within the component simulation. Due to minimum time step requirements a detailed modelling of every single joint is impossible. Thus, substitute models are used in component simulations, which describe the joints behaviour based on constrained conditions between the joining partners. However, these substitute models are developed for traditional FEA. To enable the application of IGA to vehicle simulations, this paper investigates whether existing constrained-based substitute models (e.g. *CONSTRAINED_SPR3 spotweld elements) can be combined with isogeometric shell models without further modifications. Therefore, specimen and component tests are simulated with IGA and FEA. The simulation results are compared to each other as well as experimental test results. It can be shown that IGA achieves a very good agreement with the experimental results, with a prediction quality comparable to the traditional FEA. This allows a straightforward replacement of existing FEA shell components with their IGA counterparts in vehicle simulations.
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Schenke, Simone A., Daniel Groener, Michael Grunert, and Alexander R. Stahl. "Integrated Thyroid Imaging: Ultrasound and Scintigraphy." In Integrated Diagnostics and Theranostics of Thyroid Diseases. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35213-3_4.

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AbstractThyroid sonography has made substantial progress over the last decades in terms of spatial resolution and additional parameters including vascularity, perfusion, and elasticity of lesions. The improved depictability of thyroid nodules has led to a more detailed sonographic characterization of malignant thyroid nodules considering features like microcalcification, capsular invasion, and reduced elasticity. Thus, ultrasound (US) has become the most important single tool for risk assessment of thyroid nodules. Predominantly cystic and spongiform nodules as well as many nodules with a mixed composition of solid and cystic components can safely be identified as benign on ultrasound and need no further work-up for risk assessment. Predominantly solid nodules, however, require a structured and consistent sonographic assessment including the evaluation of nodule composition, echogenicity, calcifications, shape, margins, and additional sonographic parameters. One should adhere to one of the TIRADS when reporting ultrasound findings on thyroid nodules categorizing each nodule to a certain risk class. However, the risk of malignancy reported in studies for each class may not be readily transferrable to daily routine, since the prevalence of malignant nodules in tertiary care centers is as high as 20% whereas in primary care units the prevalence may be as low as 1 per mille, thus lowering positive predictive values by one to two orders of magnitude. In addition, some suspicious features found in those studies may not be directly transferable to daily practice: microcalcifications are often difficult to discern from benign colloidal spots, even after having run through learning sessions; a taller-than-wide shape of nodules with contact to the dorsal parts of the thyroid gland does not imply malignancy according to a pole concept which has been recently developed.Thyroid scintigraphy (TS) has long been an indispensable modality for functional characterization of thyroid nodules. It should be applied to rule out malignancy in a substantial proportion of predominantly solid nodules by showing a hyperfunctioning (“hot”) nodule. Such nodules are almost always benign with few exceptions (e.g., “trapping only” nodules). If laboratory findings and scintigraphic appearance of a hot nodule do not match, further diagnostic work-up is warranted including 123I− scintigraphy with late imaging at 24 h and fine-needle aspiration cytology (FNAC). In settings with a rather low prevalence of malignant thyroid nodules such as in primary or secondary care units, a combined use of US and TS may be adopted to rule in suspicious nodules for further work-up by FNAC, including predominantly solid hypofunctioning (“cold”) nodules. MIBI scintigraphy can be used in hypofunctioning nodules with indeterminate cytology and if contraindications preclude patients from FNAC.TS is a valuable tool to detect functional abnormalities not only in nodules but also in the thyroid gland as a whole by assessing the overall radionuclide uptake. As such, it has long been used to confirm the diagnosis of Graves’ disease and thyroid autonomy. For disseminated autonomy, it is the only tool to definitely verify the diagnosis. In destructive thyroiditis, TS reliably shows a reduced overall radionuclide uptake justifying its application in selected cases with doubtful sonographic or laboratory findings. The user of TS is encouraged to calculate a site-specific normal range for the uptake value normalized to TSH in addition to the raw uptake. Thus, even subtle functional disorders of the thyroid can be detected and graded.Functional or metabolic imaging is increasingly combined with morphological imaging and is acquired and displayed as volume data rather than planar images including SPECT/CT, PET/CT, and PET/MRI. These combined modalities increase the restricted morphological field of view from ultrasound enabling to reliably image substernal, ectopic, or dystopic localizations of thyroid tissue. Combined modalities also benefit from the increasing spectrum of functional or metabolic tracers including MIBI, iodine isotopes (e.g., 124I-) and newer PET tracers.
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Chavan, Puja A., and Sharmishta Desai. "A Review on BCI Emotions Classification for EEG Signals Using Deep Learning." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210241.

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Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognition of emotions based on electroencephalogram (EEG) can predict human emotion. However, it is notable that data obtained from either nonverbal or verbal behaviors are indirect emotional signals suggesting brain activity. Unlike the nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals has become a very important research subject in current emotional brain-computer interfaces (BCIs) aimed at inferring human emotional states based on the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic disease from EEG signals. The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. Finally, system demonstrates each EEG data file as normal or epileptic disease. In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems.
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Hari, Riitta, and Aina Puce. "Brain Signals Related to Change Detection." In MEG - EEG Primer, 2nd ed., edited by Riitta Hari and Aina Puce. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197542187.003.0018.

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Abstract This chapter examines neurophysiological responses related to various cognitive processes, expectation of stimuli and events, and stimulus probability. The responses include the contingent negative variation, the electric and magnetic mismatch responses, P300, the N400 family of responses typically occurring during processing of speech and language, and the error-related negativity that arises when subjects commit an error. The attributes of these responses, the variables that modulate them, and their likely neural generators are discussed. Although these responses have been known for many decades, here they are discussed within the same predictive coding framework as they seem to be related to complex and goal-directed human behavior that unfolds in uncertain and surprising environments.
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Smiley, Aref, and Joseph Finkelstein. "Smart Wearable Analytics for Cycling: AI-Based Physical Exertion Prediction." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250714.

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We evaluated deep learning approaches for classification and regression prediction, focusing on an LSTM with Multi-Head Attention model. Data from 27 healthy participants performing cycling exercises were segmented into eight two-minute intervals. Heart rate, oxygen saturation, pedal speed (RPM), and HRV features (extracted from ECG in both frequency and time domains) served as predictive inputs. Rating of Perceived Exertion (RPE) was collected every minute and used as the predictive response, categorized into high and low exertion for classification. Physiological features and RPM from each segment were used to predict the next two-minute RPE. Feature selection via Minimum Redundancy Maximum Relevance (MRMR) and Univariate Feature Ranking (UFR) identified key predictors. The LSTM with Multi-Head Attention model achieved an MSE of 1.4 and R2 of 0.54 for regression and 82.9% accuracy with an F1 score of 86.3% for classification, demonstrating its effectiveness in exertion prediction.
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Hayashida Kohei, Nakayama Masato, Nishiura Takanobu, and Yamashita Yoichi. "Gaussian Mixture Model Learning for Desired Speech Discrimination Based on Kurtosis of Linear Prediction Residual Signals." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-405-3-367.

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Desired/undesired speech discrimination is as important as speech/non-speech discrimination to achieve useful applications such as speech interfaces and teleconferencing systems. Conventional methods for voice activity detection (VAD) utilize the directional information of sound sources to distinguish desired from undesired speech. However, these methods have to use multiple microphones to estimate the directions of sound sources. Here, we propose a new method that uses a single microphone to discriminate desired from undesired speech. We assumed that the desired talkers would be close to the microphone, so the proposed method discriminates close/distant-talking speech from observed signals with a Gaussian mixture model that was constructed by the kurtosis of the linear prediction (LP) residual signals in advance. The experimental results revealed that the proposed method could distinguish close-talking speech from distant-talking speech within a 10% equal error rate (EER) in ordinary reverberant environments.
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Conference papers on the topic "Speech prediction EEG"

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Mohan, Anand, and R. S. Anand. "Enhanced EEG Signal Spectral Visualization for Imagined Speech Task Prediction." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725791.

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Balasubramanian, Arun, Kartik Pandey, Gautam Veer, and Debasis Samanta. "Activity Prediction for Localizing the Events in Imagined Speech EEG Signals." In 2025 13th International Conference on Brain-Computer Interface (BCI). IEEE, 2025. https://doi.org/10.1109/bci65088.2025.10931413.

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Lee, Jihwan, Tiantian Feng, Aditya Kommineni, Sudarsana Reddy Kadiri, and Shrikanth Narayanan. "Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887915.

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Zhang, Guanglong, Tianren Wang, Jinjie Guo, Zhiyuan Yang, YiLian Wu, and Guixia Kang. "Exploring the Interpretability of EEG-Inception Convolutional Neural Networks for Epilepsy Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889672.

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Amit, Tomer, Taly Markovits, Guy Gurevitch, Talma Hendler, and Lior Wolf. "Predicting local fMRI activations from EEG: a Feasibility Study Using Both Classical and Modern Machine Learning Pipelines." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890237.

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Csapó, Tamás Gábor, Frigyes Viktor Arthur, Péter Nagy, and Ádám Boncz. "Towards Ultrasound Tongue Image prediction from EEG during speech production." In INTERSPEECH 2023. ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-40.

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Sakthi, Madhumitha, Ahmed Tewfik, and Bharath Chandrasekaran. "Native Language and Stimuli Signal Prediction from EEG." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682563.

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Williamson, James R., Daniel W. Bliss, and David W. Browne. "Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946491.

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Li, Ziyi, Li-Ming Zhao, Wei-Long Zheng, and Bao-Liang Lu. "Temporal-Spatial Prediction: Pre-Training on Diverse Datasets for EEG Classification." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447845.

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Merino, Lenis Mauricio, Jia Meng, Stephen Gordon, et al. "A bag-of-words model for task-load prediction from EEG in complex environments." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6637846.

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Reports on the topic "Speech prediction EEG"

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Eberlein, Robert, and Sven Düzel. Fatigue lifetime analysis of POM gears for generalized tooth root shapes. Universidad de los Andes, 2024. https://doi.org/10.51573/andes.pps39.gs.ms.1.

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The current calculation methods for determining the tooth root load capacity of polymer gears (e.g., VDI 2736) are based on the same assumptions as those for steel gears. However, due to the non-linear material behavior, temperature, and rate dependency of polymers, these predictions are often inaccurate. A previous study employed rate-dependent nonlinear viscoplastic finite element (FE) modelling of polyoxymethylene (POM) to quantify material influences not considered in standard metal gear assumptions. A lifetime model was developed and validated to predict tooth root fracture based on rotat
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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immedi
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3

LeGrand, Sandra, Theodore Letcher, Gregory Okin, et al. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47116.

Full text
Abstract:
Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immedi
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4

Bajwa, Abdullah, and Timothy Jacobs. PR-457-17201-R01 Residual Gas Fraction Estimation Based on Measured In-Cylinder Pressure. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011519.

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Abstract:
Developing a reliable means of predicting and controlling engine operation at various operating conditions may be a reasonable pathway for meeting increasingly stringent engine emission regulations. In order to do so, estimates of the mixture composition at the end of the gas exchange process (i.e. at exhaust port closure, or EPC) are helpful for having accurate models of engine combustion. A substantial fraction of the trapped charge mixture is composed of residual gases from previous cycles. This fraction, the trapped residual fraction, changes combustion characteristics of the trapped mixtu
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