Brain Invader Data
Brain Invader Data - Related Videos
09:29
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
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Cited by 7 •
2022
We report a technique permitting live imaging of microtubule dynamics in glioblastoma (GBM) cells invading a vertebrate brain tissue. Coupling the orthotopic injection of fluorescently tagged GBM cells into a transparent zebrafish brain with high-resolution intravital imaging allows the measurement of cytoskeleton dynamics during in situ cancer invasion.
03:53
Laser Microdissection: A Technique to Isolate Invading Colorectal Cancer cells from a 3D Organotypic Culture Model
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2023
The video describes the laser dissection of the 3D organotypic co-culture model with micron-level precision. In 3D organotypic models, stromal constituents such as fibroblasts are 3-dimensionally co-cultured with cancer epithelial cells. The 3D model serves as an experimental tool that enables invasion and cancer-stroma interactions to be studied in near-physiological conditions.
06:32
Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation
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Cited by 4 •
2023
The protocol shows a prototype of the at-home multi-modal data collection platform that supports research optimizing adaptive deep brain stimulation (aDBS) for people with neurological movement disorders. We also present key findings from deploying the platform for over a year to the home of an individual with Parkinson's disease.
06:09
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Cited by 1 •
2023
This article presents a method for estimating same-day P300 speller Brain-Computer Interface (BCI) accuracy using a small testing dataset.
10:35
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Cited by 49 •
2013
Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.
FAQs
The key contrast is whether EEG and behavioral measurements differ when a participant attends to a designated item versus ignores a distractor. Those differences provide evidence about target detection and attention. In the dataset, neural features can therefore be examined alongside task behavior rather than treating every visual event as equivalent, helping researchers relate stimulus status to cognitive processing.
The P300 provides a neural feature associated with detecting an attended target. Researchers can examine its event-related potential pattern to determine whether the EEG signal reflects the participant’s focus on the designated visual item. Its presence or variation is therefore relevant for studying attention, cognitive processing, and the neural basis of selection during the task.
Classification uses neural features extracted from the recorded EEG to distinguish responses linked to attended targets from responses linked to distractors. The resulting signal patterns can be related to the participant’s intended choice in the visual task. This makes the data useful for evaluating whether brain activity can provide reliable information about selection without relying only on observed behavior.
Behavioral measurements show how participants perform during the task, while neurophysiological measurements reveal associated brain responses. Considering both can connect observable task outcomes with EEG features related to attention and target detection. This combined perspective helps researchers evaluate cognitive processing and BCI performance more fully than either measurement type would provide alone.
Analysis begins with the behavioral records and EEG measurements collected during the visual game. Researchers distinguish events involving designated targets from those involving distractors, then examine event-related potentials and other signal features associated with those events. These features can subsequently support comparisons across participants or conditions and classification of the user’s intended selection.
Comparing Brain Invader data across participants, sessions, or experimental conditions shows how attention-related EEG features and task performance vary in different contexts. Such comparisons can help researchers evaluate the consistency of target-related responses and BCI performance. They also provide a framework for determining whether observed patterns are specific to one participant or persist across measurements.
These data support research on attention, cognitive processing, EEG-based signal analysis, and BCI performance. In neuroscience, investigators can relate P300 responses and other neural features to the detection of attended visual targets. In BCI research, the same measurements help assess whether brain signals can be classified to represent a user’s intended selection during the task.