Multivariate time-series analysis and diffusion maps

Lian, Wenzhao; Talmon, Ronen; Zaveri, Hitten P.; Carin, Lawrence; Coifman, Ronald R.

doi:10.1016/j.sigpro.2015.04.003

Cited by 25 publications

(27 citation statements)

References 42 publications

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“…As such, we used EEG potential because it represents the changes of brain states over time. Recent studies have shown that time domain analysis plays an important role in understanding the EEG signals [ 28 – 31 ]. These studies have applied many time analysis methods on EEG recorded during performing various tasks (such as problem solving [ 29 ]) and brain states (e.g., emotional states [ 30 ] and epileptic seizure [ 32 ]).…”

Section: Methodsmentioning

confidence: 99%

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Bamatraf

Hussain

Aboalsamh

et al. 2016

Computational Intelligence and Neuroscience

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We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

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Section: Methodsmentioning

confidence: 99%

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Bamatraf

Hussain

Aboalsamh

et al. 2016

Computational Intelligence and Neuroscience

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“…DM has been extended to dynamical systems previously [13][14][15][16]. In particular, Talmon and Coifman [14,15] introduced an approach called empirical intrinsic geometry (EIG) that builds a diffusion geometry using a noise resilient distance.…”

Section: Related Workmentioning

confidence: 99%

Visualizing High Dimensional Dynamical Processes

Duque

Wolf

Moon

2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years. While these methods are effective at learning a lowdimensional representation, they are often insufficient for visualizing the global and local structure of the data. In this paper, we present DIG (Dynamical Information Geometry), a visualization method for multivariate time series data that extracts an information geometry from a diffusion framework. Specifically, we implement a novel group of distances in the context of diffusion operators, which may be useful to reveal structure in the data that may not be accessible by the commonly used diffusion distances. Finally, we present a case study applying our visualization tool to EEG data to visualize sleep stages.

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“…Technically, this kernel form has been considered in several related works such as [30,14,21,19,15,27] and references therein. These works consider the relations between the analyzed dataset and a reference set, which typically is significantly smaller than the dataset.…”

Section: Definition 31 (Measure-based Gaussian Correlation Kernel)mentioning

confidence: 99%