Decoding covert visual attention based on phase transfer entropy

Ahmadi, Amirmasoud; Davoudi, Saeideh; Behroozi, Mahsa; Daliri, Mohammad Reza

doi:10.1016/j.physbeh.2020.112932

Cited by 19 publications

(10 citation statements)

References 88 publications

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“…Second, estimating such a large number of pairwise channel connectivities can be computationally expensive, especially for measures such as TE and single-trial TE θ [8], which can hinder their inclusion in practical BCI systems. Both problems could be addressed by identifying the set of pairwise channel connectivities that are relevant to discriminate between specific conditions, which would also lead to a clearer neurophysiological interpretation of the obtained results [6,10]. To that end, we explore a relevance analysis strategy based on centered kernel alignment (CKA).…”

Section: Kernel-based Relevance Analysismentioning

confidence: 99%

“…Three properties characterize such oscillations: amplitude, frequency, and phase, the latter referring to the position of a signal within an oscillation cycle [5]. Oscillation amplitudes are related to neural synchrony expansion in a local assembly, while the relationships between the phases of neural oscillations, such as phase synchronization, are involved in the coordination of anatomically distributed processing [6]. Moreover, from a functional perspective, phase Appl.…”

Section: Introductionmentioning

confidence: 99%

“…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. Nonetheless, binning estimators for single trial-based estimation of information-theoretic measures exhibit systematic bias [8].…”

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: Kernel-based Relevance Analysismentioning

confidence: 99%

Section: Introductionmentioning

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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“…Recent research has investigated the directional information transfer by the dPTE measurement in the brain regions on the Magnetoencephalography (MEG) in various brain disorders including, Alzheimer's and Parkinson's [20][21][22]. Dauwan et al (2016) used the dPTE to examine the connectivity of brain regions in dementia with Lewy bodies [23] and Ahmadi et al (2020) applied the dPTE method to decode covert visual attention based on EEG [24]. In more recent researches, the Phase Transfer Entropy (PTE) measure and the graph theory have been used to investigate the brain networks in brain disorders with the EEG signals [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Ekhlasi

Nasrabadi

Mohammadi

2021

fbt

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Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

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“…It focuses on spatial positioning (spatial attention) or target-defining features (feature-based attention) across the visual cortex [1], [2]. Visual attention as a cognitive factor plays a substantial role in the higher-order mental information processing that occurs in the brain [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Single-Trial Decoding of Motion Direction During Visual Attention From Local Field Potential Signals

2021

Self Cite

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Brain-Computer Interface (BCI) based on Local Field Potential (LFP) has recently been developed to restore communication or behavioral functions. LFP provides comprehensive information, due to its stability, robustness, and reach frequency content within the cognitive process. It has been demonstrated that spatial attention can be decoded from brain activity in the visual cortical areas. However, whether motion direction can be decoded from the LFP signal in the primate visual cortex remains uninvestigated, as well as how decoding performance may be influenced by spatial attention. In this paper, these issues were examined by recording LFP from the middle temporal area (MT) of macaque, employing machine learning algorithms. The animal was trained to report a brief direction change in a target stimulus which moved in various directions during a visual attention task. It was found that the LFP-gamma power was able to provide significant information to reliably decode motion direction, compared with other frequency bands, on a single-trial basis. Moreover, the results show that spatial attention leads to enhancements in motion direction discrimination performance. The highest decoding performance was achieved in the high-gamma frequencies (60-120Hz) when targets were presented inside the receptive field in opposite directions. Using a feature selection approach, performance was improved by optimally selecting features where the highest level of participation was observed in the gamma-band. Generally, the results suggest that in the MT area, LFP signals exhibit appreciable information about visual features like motion direction, which could thus be utilized as a control signal for cognitive BCI systems.INDEX TERMS Motion direction decoding, local field potential, brain-computer interface, spatial attention, visual area MT.

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Decoding covert visual attention based on phase transfer entropy

Cited by 19 publications

References 88 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

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Single-Trial Decoding of Motion Direction During Visual Attention From Local Field Potential Signals

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