Electroencephalography Biomarkers
Electroencephalography Biomarkers - Related Videos
10:22
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Cited by 59 •
2016
High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.
06:37
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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2023
The experimental protocol demonstrates the paradigm for acquiring and analyzing electroencephalography (EEG) signals during upper limb movement in individuals with stroke. The alteration of the functional network of low-beta EEG frequency bands was observed during the movement of the impaired upper limb and was associated with the degree of motor impairment.
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
04:45
Assessing Brain Hyperexcitability with Transcranial Magnetic Stimulation and Electroencephalography
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2025
Source: Shafi, M. M., et al. A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy. J. Vis. Exp. (2016)The video demonstrates the procedure for using transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to assess neuronal excitability in patients with epilepsy. It details the steps for aligning the patient’s head with a pre-recorded brain scan, positioning the TMS coil over a functionally connected...
08:28
An Overview of bGDGT Biomarker Analysis for Paleoclimatology
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types...
FAQs
The measurable signal reflects coordinated postsynaptic activity across populations of cortical neurons rather than the activity of a single cell. Electrode recordings capture this collective electrical behavior, which can then be analyzed for recurring patterns. These patterns provide engineering systems with measurable indicators of neural states or processes, although their reliability depends on how consistently the underlying activity is recorded.
These measures describe different properties of the same EEG data. Frequency-band power summarizes activity distributed across frequency ranges, while event-related potentials characterize responses associated with events. Connectivity examines relationships among recorded signals, and signal complexity quantifies aspects of signal structure. Selecting among them depends on whether an application needs state characterization, event detection, relational information, or a broader description of signal behavior.
EEG biomarker estimates can be affected by unwanted components introduced during signal acquisition, so artifact reduction helps separate relevant neural patterns from contamination. Validation then tests whether an extracted feature reliably characterizes the intended neural state or process. Together, these steps reduce the risk that an engineering system bases classifications or device-control commands on recording conditions rather than meaningful brain activity.
The choice should match the information required by the system. Frequency-band power may support analysis of activity across frequency ranges, event-related potentials may be appropriate when responses are linked to events, and connectivity or complexity may provide relational or structural descriptions. Comparing these representations helps engineers identify features that best support the intended classification, communication function, or control task.
A basic workflow begins with EEG signal acquisition through scalp electrodes, followed by artifact reduction to improve signal quality. Engineers then calculate selected features, such as power, event-related potentials, connectivity, or complexity, and validate their relationship to the neural state or process of interest. The resulting features can support classification or translation into device-control commands.
They are useful when an engineering system must infer meaningful information from neural activity without relying solely on conventional physical input. Extracted features can help characterize cognitive or neurological function, support brain-computer interfaces, and contribute to assistive communication technologies. In these settings, the biomarker serves as an input to systems designed to classify neural activity or control a device.
Analysis can produce quantitative features for characterizing neural states or processes and can support classifications derived from recorded brain activity. In neurotechnology, these outputs may be translated into meaningful system inputs or device-control commands. Their value depends on careful acquisition, feature analysis, artifact reduction, and validation, which connect the recorded signal to the intended cognitive, neurological, or engineering outcome.