84 results for "Theta Rhythm" · page 6 of 9 · ranked by relevance
…AR, DTF, PLV - **Frequency bands**: theta=[4.0, 5.0] Hz; alpha…
This dataset contains 64-channel EEG recordings from 36 participants performing a visual tilt illusion task, in which the perceived orientation of a central grating was biased by a surrounding annular grating. The study examines how prestimulus alpha oscillation power and phase relate to discrimination performance and the magnitude of the tilt illusion. Data were collected to support investigations into the neural correlates of perceptual bias in orientation discrimination.
…regularized linear discriminant analysis, sensorimotor rhythms ## References Scherer, R., Faller, J., Balderas…
The Alphawaves dataset comprises resting-state EEG recordings from 19 healthy participants acquired using a 16-channel g.tec g.USBamp system at 512 Hz sampling rate. Participants alternated between eyes-closed and eyes-open conditions in 10-second trials, with event-related synchronization analysis focused on alpha frequency band activity (8-12 Hz). This dataset serves as a benchmark resource for brain-computer interface research and resting-state EEG analysis.
…quickly in their self-determined rhythm without speaking or using finger movements…
An open-access hybrid brain-computer interface dataset combining electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings from 29 healthy subjects performing motor imagery and mental arithmetic tasks. The dataset includes preprocessed EEG data (30 EEG channels plus 2 EOG channels, 200 Hz sampling rate) and NIRS measurements (36 channels) across six sessions per subject, with validated classification performance demonstrating enhanced discrimination of mental states through multimodal analysis compared to single-modality approaches.
…analyzed=[2.0, 28.0] Hz; theta=[4.0, 8.0] Hz…
Imported from OpenNeuro ds004854
This dataset comprises 12-class steady-state visual evoked potential (SSVEP) recordings acquired from 10 healthy subjects using 8 EEG channels during a brain-computer interface task. The data were collected to evaluate and compare canonical correlation analysis (CCA)-based methods for SSVEP detection. The dataset includes preprocessed EEG signals with joint frequency-phase modulated visual stimuli ranging from 9.25 to 14.75 Hz. In the reference study, CCA-based methods achieved 92.78% classification accuracy with an information transfer rate of 91.68 bits/min using a combination approach.
This dataset contains simultaneous EEG and MEG recordings from twenty adult participants presented with harmonic complex sound (HCS) stimuli varying in frequency, in an auditory oddball paradigm. The study investigates pre-attentive pitch processing at both sensor and source levels, comparing EEG and MEG responses. Data acquisition was conducted at the MEG Centre, Timone Hospital, Marseille, France.