Since the development of the electroencephalogram (EEG), electrical signals have been obtained by gradually more invasive methods and at increasingly higher spatial resolution: from recording electrical activity from the top of the skull (EEG) or the surface of the cerebral cortex (i.e. Brain signals vary widely in their spatiotemporal characteristics and means of acquisition. The performance of the software is compared against semi-manual analysis and validated by verification of prior biological knowledge.Įlectrical brain activity recordings provide important diagnostic and research tools. In addition, it is fast, highly efficient and reproducible. The developed method is based on the implementation of established signal processing and machine learning approaches, is fully automated and depends solely on the data. ![]() slow analysis speed, arbitrary threshold-based detection and lack of reproducibility across and within experiments). Here, we present a computational method for the automatic detection and quantification of in-vitro LFP events, aiming to overcome the limitations of current approaches (e.g. The local field potential (LFP) is a particularly attractive method for recording network activity, because it allows for long and stable recordings from multiple sites, allowing researchers to estimate the functional connectivity of local networks. A key element in such studies is the accurate determination of the timing and duration of those network events. ![]() Synchronized brain activity in the form of alternating epochs of massive persistent network activity and periods of generalized neural silence, has been extensively studied as a fundamental form of circuit dynamics, important for many cognitive functions including short-term memory, memory consolidation, or attentional modulation.
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