Showing 10 of 764 datasets · page 3 of 77
This dataset comprises EEG recordings organized in BIDS format, collected from participants at pre- and post-intervention time points. It is intended to support analyses of intervention-related changes in brain activity across two experimental groups.
This dataset comprises MEG recordings from a cross-modal target-discrimination study investigating alpha oscillations and frequency-tagged neural responses during auditory and visual target preparation. Participants were cued to attend to either auditory or visual targets, with a task-irrelevant 3-second interval featuring frequency-tagged visual (36 Hz) and auditory (40 Hz) stimulation prior to target onset. The study examined target discrimination performance under conditions with and without cross-modal distractors, recorded at the University of Birmingham, United Kingdom.
MorseEEG-ATP is a multitask EEG dataset for investigating auditory temporal processing and temporal pattern discrimination using Morse code stimuli. It contains synchronously recorded EEG and behavioral data from 32 healthy adults performing two auditory discrimination tasks (dot-count and first-last match) across three presentation-rate conditions (15, 30, and 40 characters per minute). The dataset, organized in BIDS format, includes raw and minimally preprocessed EEG data, trial-aligned event annotations, behavioral measures, and analysis scripts to support research on temporal pattern discrimination and listening ability assessment.
This dataset contains EEG recordings from a crossmodal target-discrimination study investigating attentional preparation and alpha oscillations. Participants were cued to attend to auditory, visual, or non-specific targets, with frequency-tagged stimuli (36 Hz visual, 40 Hz auditory) presented during a preparatory interval preceding target onset. The study examines how cross-modal attention and target expectation modulate neural oscillatory activity and behavioral performance.
This dataset contains EEG recordings from forty participants performing a gambling task involving repeated door selections and monetary feedback. Blocks were either learnable, where one door yielded rewards more frequently, or unlearnable, where outcomes were randomly drawn but matched in overall win/loss totals. After each block, participants completed a brief survey on perceived learning, enjoyment, motivation, and performance. The dataset supports investigation of decision-making and reward learning processes reflected in EEG signals.
This dataset investigates brain signal-based emotion recognition through magnetoencephalography (MEG) recordings collected while participants viewed validated emotional video stimuli. It comprises three components: a large-scale online behavioral survey of 500 participants rating 40 video clips, head digitization data for co-registration, and MEG neural recordings from 23 participants viewing the same stimuli. Emotional states were assessed using Self-Assessment Manikin ratings, discrete emotion categories (PrEmo), and temporal highlight annotations, providing multi-faceted ground truth for affective neuroscience research.
ROAMM is a large-scale multimodal dataset combining simultaneous EEG and eye-tracking recordings collected during naturalistic reading, with span-level mind-wandering annotations from 44 participants. It provides a benchmark for mind-wandering detection and EEG-to-text decoding, supporting research on attention-related degradation in language decoding from brain activity during naturalistic reading.
This dataset comprises resting-state and loudness dependence of auditory evoked potentials (LDAEP) EEG recordings from 69 healthy women aged 19-40, collected at the University of Oslo. It was assembled to investigate the relationship between hormonal contraceptive use and central serotonergic activity as indexed by LDAEP. In addition to EEG data, the dataset includes phenotype data on hormonal contraceptive use, menstrual cycle phase, depressive symptoms (BDI-II), impulsivity (UPPS-P), and lifestyle factors.
This dataset comprises resting-state EEG recordings from 51 right-handed undergraduate participants collected at the University of Alberta, examining relationships between alpha/theta oscillatory dynamics and trait anxiety. Recordings include alternating eyes-open and eyes-closed resting blocks, accompanied by anxiety and personality questionnaire data. The dataset supports investigation into EEG correlates of trait anxiety and related psychological constructs.
This dataset comprises 128-channel EEG recordings from 98 participants recruited from the COVIDOM/NAPKON-POP cohort, all with a history of confirmed SARS-CoV-2 infection. The study investigates neurophysiological correlates of Post-COVID-19 condition, focusing on self-reported cognitive symptoms, fatigue, anxiety, depression, and sleep quality. Participants underwent EEG recording during resting-state and cognitive tasks including a Psychomotor Vigilance Task, alongside standardized questionnaires (FACIT-Fatigue, HADS, PSQI) and neuropsychological testing.