![]() The goal of this Research Topic is to focus on cutting-edge noise removal, feature extraction, and classification techniques using both machine learning and deep learning to improve the accuracy, reliability, information transfer rate, and overall performance of brain computer interface (BCI) systems based on non-invasive methods like spike trains, LFP (local field potential), EEG (electroencephalography), MEG (magnetoencephalography), ECoG (electrocorticography), and fMRI (functional magnetic resonance imaging). Contrary to conventional assumptions, neural signal processing frequently works with neurophysiological data that is non-Gaussian, non-stationary, and heterogeneous. Statistical signal processing, statistics, control, and optimization techniques are all used in the developing field of neural signal processing to handle neural or neuronal data from various sources. Neural signal processing in neuroscience aims to extract information from neural signals in order to better understand how the brain represents and conveys information through neuronal ensembles. With the complexity and size of brain recordings growing, the use of neural signal processing has gained significance in the area of neuroscience. Many forms of neuron signals, such as spike trains, LFP (local field potential), EEG (electroencephalography), MEG (magnetoencephalography), ECoG (electrocorticography), fMRI (functional magnetic resonance imaging), and calcium imaging, can be used to identify human brain activity. Synapses are the connections that allow neurons in the human brain to communicate with one another. A sophisticated network of neurons underlies the brain's capacity to interpret sensory input and control movement. The human brain is one of the most complicated systems ever explored.
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