100 results for "picture-word interference" · page 7 of 10 · ranked by relevance
…Specifically, a random word list consisting of the unique content words (nouns…
This dataset comprises preprocessed EEG recordings from 8 healthy subjects performing imagined speech tasks involving three vowel phonemes (a, i, u). Participants received auditory and visual cues to imagine speaking each vowel, with 64-channel EEG data acquired at 256 Hz. The dataset includes 2,400 trials generating 7,200 overlapping 2-second epochs (8 subjects × 300 trials × 3 overlapping epochs per trial) analyzed using Riemannian manifold-based feature extraction and relevance vector machine classification for brain-computer interface applications, achieving approximately 49% mean accuracy across a 10-fold cross-validation scheme.
…10.82901/nemar.on007052) # README Word Processing Task from the PURSUE project…
This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.
This dataset contains EEG recordings from 34 participants collected to investigate the adaptive recruitment of cortex-wide recurrent processing during visual object recognition. Participants viewed a stimulus set of 242 images, comprising 'challenge' and 'control' images selected based on discrepancies between human behavioral performance and AlexNet classification, while performing a rapid serial visual presentation task with a paper-clip detection component. The dataset includes derivatives with time-resolved decoding accuracy matrices for object identity, supporting analyses of recurrent cortical dynamics in visual processing.
…Sentences were presented in an RSVP design, one word at a time…
…On each trial, a red prime word was followed by a green…
This dataset contains EEG recordings from 23 participants performing a passive viewing task involving partially occluded and uncovered visual scenes containing varying numbers of game pieces. The study investigates how the brain processes numerosity information under conditions of occlusion and disocclusion. Participants viewed scenes with 4 or 32 initially visible game pieces, followed by uncovering to reveal different numbers of additional pieces, across 640 trials.
This dataset contains EEG recordings from participants performing a novel instruction-following task and an accompanying 1-back localizer task, designed to investigate how complex task instructions are represented and implemented in the brain. Each participant completed a single session including 16 blocks of the main instruction-following task, requiring integration or selection of visual features across sequential instruction screens, and 8 blocks of a localizer task. The dataset is intended to support research on instruction-based cognitive control and rule representation.