Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

García-Murillo, Daniel Guillermo; Álvarez-Meza, Andrés Marino; Castellanos-Domínguez, Germán

doi:10.3390/s21082750

Cited by 10 publications

(12 citation statements)

References 58 publications

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“…Therefore, it is important to reduce as much as possible the number of channel pair connectivity features required to achieve peak classification performance. Additionally, it is important to highlight that while classification accuracies in Figure 6, and in Table 1, are in the same range of those obtained through other connectivitybased characterization approaches [10,23], they are far below those obtained from methods such as common spatial patterns [59][60][61]. A possible explanation is that bivariate TE might be more robust at describing long-range interactions rather than local ones [41], like those arising from MI-related activity, centered on the sensorimotor area.…”

Section: Eeg Data Resultsmentioning

confidence: 80%

“…That is to say, we can reconstruct normalized relevance connectivity matrices by properly reshaping , so as to visualize the connectivity pairs and frequency ranges that are discriminant for the task of interest. In that line, we followed the approach proposed in [23] to interpret the relevance information by clustering the subjects according to common relevance patterns.…”

Section: Eeg Data Resultsmentioning

confidence: 99%

“…Discriminative information may be hidden in the dynamical interactions among spatially separated brain regions that characterization methods commonly employed in BCI are not able detect [22]. This information could be relevant to address issues such as the inefficiency problem in some BCI systems [23]. In that context, authors in [6] applied a binning strategy to estimate single-trial phase TE to set up classification systems for visual attention.…”

Section: Introductionmentioning

confidence: 99%

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Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

et al. 2021

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Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.

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Section: Eeg Data Resultsmentioning

confidence: 80%

Section: Eeg Data Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

et al. 2021

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“…This enables the measure to reveal temporal changes of connectivity and opens a new direction for further research. This can be especially useful for analyzing human brain networks in auditory and visual tasks [39,40] and is also promising for assessing motor skills [41]. This also allows us to observe changes in the organization of brain network connectivity over time using well-known measures from complex network graph theory [42].…”

Section: Discussionmentioning

confidence: 99%

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

Šverko

Vrankić

Vlahinić

et al. 2022

Sensors

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In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI—not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index.

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“…Moreover, EHECCO-based HAR applications from conventional video camaras [ 8 ], WiFi human sensing [ 10 ], and RFID [ 11 ] data will be carried out. Further, we plan to test the EHECCO metric on other types of time series, i.e., brain activity data [ 67 ]. Additionally, more elaborate classifiers and deep learning schemes can benefit from our EHECCO metric [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Valencia-Marin

Pulgarín-Giraldo

Velásquez-Martínez

et al. 2021

Sensors

Self Cite

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Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class).

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Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

Cited by 10 publications

References 58 publications

Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

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