85 results for "adaptive neurostimulation" · page 4 of 9 · ranked by relevance
…60 Hz notch filtering, and adaptive EOG artifact removal. Feature extraction employed…
A large-scale longitudinal dataset of sensorimotor rhythm-based brain-computer interface (BCI) training in 62 healthy adults. The dataset comprises over 600 hours of EEG recordings across 598 sessions with more than 250,000 trials of motor imagery tasks (left hand, right hand, both hands, and rest). This resource enables investigation of BCI learning dynamics and algorithm development for non-invasive neural control applications.
…real-time adaptive RMDM classifier (calibration-free procedure) ## BCI Application - **Applications**: gaming…
This dataset contains electrocorticography (ECoG) recordings from three cohorts of C57BL/6 mice examining how transcranially delivered diagnostic ultrasound (tDUS) modulates cortical responses to a blinking light stimulus. Mice were exposed to combinations of tDUS and light stimulation, with electrodes targeting visual and somatosensory cortices, to investigate ultrasound's potential to intentionally modulate brain activity for future clinical and therapeutic applications.
[. The experiment investigates brain entrainment responses to three stimulus modalities: auditory (5kHz carrier amplitude-modulated at 40Hz), visual (20Hz flickering white LED array), and combined audio-visual stimulation. Each stimulus epoch lasted 40 seconds and was recorded using 19 monopolar EEG channels at 500Hz sampling rate. The study aims to characterize synchronized brain oscillations induced by different sensory modalities, with potential implications for understanding gamma entrainment mechanisms relevant to neurodegenerative diseases such as Alzheimer's disease.
This dataset presents a case study examining the physiological effects of directed therapeutic intent, as practiced by a self-identified energy medicine practitioner, on live versus damaged (irradiated) cell cultures. Using a double-blind, randomized design, the practitioner underwent 60 sessions total (40 with live cells, 10 with medium only, and 10 with dead/x-rayed cells), each structured into baseline, treatment, and control segments while physiological recordings were obtained. The study aimed to investigate potential correlations between practitioner physiology and cellular outcomes under controlled conditions.
This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.