100 results for "brain language processing" · page 4 of 10 · ranked by relevance
…Low-frequency cortical entrainment to speech reflects phoneme-level processing. Current Biology…
…Whole-Brain Millisecond-Scale Effective Connectivity Atlases of Speech
This dataset comprises 128-channel EEG recordings acquired during speech production tasks involving overt, minimally overt, and covert speech conditions. Participants produced one of five color words with each trial consisting of five repetitions. The dataset includes calibration and online real-time decoding sessions, along with supplementary physiological recordings (EOG, EMG, microphone) and trigger markers, designed to investigate neural contributions to EEG-based speech decoding.
This dataset comprises electroencephalography (EEG) recordings from 7 participants performing an auditory imagery task, wherein subjects imagined sounds produced by objects from semantic categories (animals and tools) for 5-second intervals. EEG signals were acquired using a 64-channel BioSemi ActiveTwo system sampled at 2048 Hz, with concurrent electrooculography and physiological monitoring. The dataset supports research in semantic decoding and brain-computer interface applications using imagined auditory stimuli.
[ investigating speech decoding through phoneme discrimination tasks combined with transcranial magnetic stimulation (TMS). Study 1 involved 8 participants performing discrimination of consonant-vowel and vowel-consonant phoneme pairs with TMS targeting motor cortex regions (lip and tongue motor areas). Study 2 expanded to 16 participants and included single phonemes, phoneme pairs, and phoneme triplets (real and pseudowords) with additional TMS targets in Broca's area and verbal memory regions. The dataset provides a comprehensive resource for investigating the neural mechanisms underlying speech perception and articulation.
This dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech discrimination tasks between short and long words ('cooperate' vs 'in'). Data were acquired at 256 Hz using 64 EEG channels with standard preprocessing including bandpass filtering (8-70 Hz), notch filtering (60 Hz), and artifact removal. The dataset contains 1,200 trials analyzed using Riemannian manifold and relevance vector machine approaches for brain-computer interface applications, achieving mean classification accuracy of 73.3±8.9%.
This dataset contains preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks with three short word conditions (out, in, up). Data were acquired at 256 Hz using 64 channels and analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications. The motor imagery paradigm employed auditory cueing, yielding 1,800 trials suitable for BCI research and benchmarking.
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