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Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
Long-term or "continuous" video-electroencephalography (EEG) monitoring is a diagnostic technique commonly used in patients with epilepsy.It involves the long-term hospitalization of the patient, typically for days or weeks, during which brain waves are recorded via EEG and physical actions are continuously monitored by video.
In research, currently EEG is often used in combination with machine learning. [124] EEG data are pre-processed then passed on to machine learning algorithms. These algorithms are then trained to recognize different diseases like schizophrenia, [125] epilepsy [126] or dementia. [127] Furthermore, they are increasingly used to study seizure ...
EEG electrode positions in the 10-10 system using modified combinatorial nomenclature, along with the fiducials and associated lobes of the brain. When recording a more detailed EEG with more electrodes, extra electrodes are added using the 10% division , which fills in intermediate sites halfway between those of the existing 10–20 system.
EEG-based BCI approaches, together with advances in machine learning and other technologies such as wireless recording, aim to contribute to the daily lives of people with disabilities and significantly improve their quality of life. [29] Such an EEG-based BCI can help, e.g., patients with amyotrophic lateral sclerosis, with some daily activities.
A paper published in 2023 showed that burst suppression and epilepsy may share the same ephaptic coupling mechanism. [6] When inhibitory control is sufficiently low, as in the case of certain general anesthetics such as sevoflurane (due to a decrease in the firing of interneurons [7]), electric fields are able to recruit neighboring cells to fire synchronously, in a burst suppression pattern.
Brain fingerprinting (BF) is a lie detection technique which uses brain waves from a electroencephalography (EEG) to determine whether specific information is stored in the subject's cognitive memory. It was invented by Larry Farwell, a Harvard-graduated neuroscientist, and published in 1995. [1]
The EEG proved to be a useful source in recording brain activity over the ensuing decades. However, it tended to be very difficult to assess the highly specific neural process that are the focus of cognitive neuroscience because using pure EEG data made it difficult to isolate individual neurocognitive processes. Event-related potentials (ERPs ...