During the last few decades, EEG research has evolved considerably. It has been used to identify and classify psychiatric and criminal subgroups, and to detect lower-order mental processes. It has also been used to develop a better understanding of the relationship between EEG and neuroimaging.
Typical EEG applications utilize an analog-to-digital conversion at a sampling rate of around 200 Hz, after which the signal is further conditioned. The sampling rate of an analog-to-digital converter is largely determined by the IC used, which affects its precision. In case of EEG check AI eeg by BrainAcces.ai.
A sampling rate of three to four times the highest expected signal frequency is recommended for practical implementations. However, this number depends on the data volume capabilities of the computer system.
In the case of BCI research, clean EEG data is of utmost importance. To achieve this, researchers need to consider artifacts. The main source of technical artifacts is external electromagnetic noise.
A high sampling rate is also required for measuring rapidly changing events. The Nyquist theorem is a mathematically derived rule that requires sampling a signal at a frequency greater than twice the highest frequency component in the original signal.
Detection of lower-order mental processes
Detection of lower-order mental processes in EEG research can be challenging. There are many factors that affect the number of trials that a cognitive task requires. In addition, the amount of gross motor movement that a task requires may require additional consideration. For example, the amount of eye blinks and lateral movements must be taken into consideration.
Identifying processing stages in a cognitive task is a longstanding tradition in cognitive science. More recent methods have applied neuroimaging to discover processing stages. These techniques are applied to both upper and lower-order cognitive processes.
To determine the processing stages of a task, researchers can use Event Related Potentials (ERPs). ERPs are waves of brain electrical activity. They are time-locked to specific stimuli. They can be displayed as a topographic map or voltage over time. You can read more about EEG on BrainAccess.
Classification of psychiatric or criminal subgroups
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Comparison of EEG recordings with those of normal children
Several epilepsy syndromes are present in childhood. Some are well accepted, while others are controversial. Many of these are not included in the current ILAE classification systems. The development of new classifications will be informed by future developments in imaging.
The EEG may show abnormalities in one or more regions. This may indicate the need for a brain scan. Some patients show discharges during sleep. However, in a small percentage of patients, these discharges are not seen.
In children with behavioural problems or neurological disorders, non-epileptic abnormalities may be recorded. These non-epileptic abnormalities may include a slow wave discharge, persistent interictal discharges, or PPR.
To determine whether these characteristics are predictive of an outcome, we constructed binary logistic regression models to assess the predictive capability of the EEG parameters. We also performed within-participant analyses in SAS software.
Relationship between EEG and neuroimaging
MRI is a non-invasive neuroimaging technique that offers direct measurement of brain structure and function. In addition to its spatial resolution, MRI also provides non-invasive measurement of tissue properties, including metabolites, cellular activity, and neuronal activation. It is also used to assess the function of the white matter, which contains important biomarkers for epilepsy.
EEG, or electro-encephalography, is a technique that measures electrical activity of tens of thousands of neurons. Electrodes are connected to the scalp and are attached to cerebrospinal fluid (CSF). Electrodes can record activity in different frequency bands. It does not allow for the detection of individual neurons, however.
Neuroimaging research uses various methodologies, each with its own strengths and limitations. EEG, MRI, and MEG are all excellent for recording activity from large populations of neurons. The differences between the methods are largely related to the spatial resolution.