… In this study, both classical machine learning algorithms and artificial neural networks were
employed for SOZ identification based on PAC features. In detail, five models including SVM …

… joint tracking of amplitude, phase and frequency. The … phase noise even in the presence
of low signal-to-noise ratio (SNR). We also demonstrate joint tracking of amplitude and phase …

… range of amplitude and phase time series as ∆݂஺ and ∆݂௉ respectively. In the second step,
we need to extract amplitude time series of ܺሺݐሻ in ∆݂஺ band (ܴ∆௙ಲሺݐሻ) and phase times …

… the full (subdivided) phase space. Again, it is clear that subdividing the phase space is very
… benefit of subdividing the phase space and training separate machines on the subregions is …

… is to exploit machine learning to develop a modelfree, fully data-based paradigm to predict
amplitude … In the prediction phase, we input the new parameter value p0 + p into the reservoir …

… Classification is the most popular supervised learning problem domain in machine learning
(… testing accuracies of the three phases with 14 electrodes for the above-discussed machine …

… To collect a data set with a large variety of LPS profiles we scan the values of the L1S phase
… the ML model are amplitude and phase readings from L1s and amplitude readings from the …

… Decoding patterns reveal functional overlap and discrepancies across phase, amplitude
and PAC The machine learning framework allowed for deeper investigation of the role of our …

… but rather the occurrence of a very high pulse whenever the trajectory approached a particular
region of the phase space. To shed light on the limits of the forecast of extreme events, we …

… for simultaneously observing both amplitude and phase without any imaging optics, based
on machine learning. In the proposed method, an object with a complex-amplitude field is …