100 results for "encoding model" · page 3 of 10 · ranked by relevance
…g.tec medical engineering GmbH - **Cap model**: g.GAMMAsys/g.LADYbird - **Electrode…
…After collecting sufficient training data, classifier models predict recall in closed-loop…
…identified by a time-resolved encoding model. *Nature Communications*. http://doi.org…
This dataset contains neuroimaging data from an experiment investigating the neural processing of numbers, letters, and false fonts presented as single items or strings. The study employs event-related experimental design with two runs, each utilizing distinct stimulus categories marked by specific trigger codes to enable precise temporal analysis of brain responses to different visual stimuli. Data were acquired using magnetoencephalography (MEG).
…Generalizable EEG encoding models with naturalistic audiovisual stimuli. Journal of Neuroscience, 41…
…Numerous datasets are publicly available for any researcher to design models for…
This dataset contains 128-channel EEG recordings from 20 observers (19 included in final analysis) viewing object images at 3.33 Hz to investigate how contextual associations, perceptual attributes, and conceptual properties of objects are represented in neural activity. One participant was excluded due to a technical error in EEG recording. Time-resolved neural decoding was applied to disentangle these distinct representational dimensions from the EEG signals.
…To better understand masking’s effects on the subcortical neural encoding of…
This dataset comprises EEG recordings from 20 healthy participants performing motor imagery of handwritten letter production. Participants imagined writing ten different letters (a, d, e, f, j, n, o, s, t, v) using their right index finger in response to visual cues. The study investigates brain-computer interface applications through direct EEG classification and continuous kinematic decoding approaches, achieving classification accuracies of 26.2% for ten-letter and 46.7% for five-letter tasks.
This dataset contains MEG recordings from a study investigating how the human brain compresses regular binary sound sequences in working memory, testing the language of thought hypothesis. Participants listened to hierarchically structured sequences of two sounds varying in complexity, quantified via minimal description length, while occasional deviant sounds probed their internalized knowledge of sequence structure. The study aimed to characterize how brain activity relates to sequence complexity and predictive processing.