Mental State Classification Using Multi-Graph Features

Chen, Guodong; Helm, Hayden S.; Lytvynets, Kate; Yang, Weiwei; Priebe, Carey E.

doi:10.3389/fnhum.2022.930291

Cited by 4 publications

(5 citation statements)

References 37 publications

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“…The descriptions of the cognitive load and stress datasets are altered versions of the descriptions found in Chen et al [47]. Unless otherwise stated, the balanced accuracy and the convex coefficient corresponding to each method are calculated using 100 different train-test splits for each participant.…”

Section: Applications To Physiological Prediction Problemsmentioning

confidence: 99%

Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Helm,

de Silva,

Vogelstein

et al. 2024

Mathematics

Self Cite

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We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifier trained on the limited data available for the target task. We derive the expected loss of an element in the class with respect to the target distribution for a specific generative model, propose a computable approximation of the loss, and demonstrate that the element of the proposed class that minimizes the approximated risk is able to exploit a natural bias–variance trade-off in task space in both simulated and real-data settings. We conclude by discussing further applications, limitations, and potential future research directions.

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Section: Applications To Physiological Prediction Problemsmentioning

confidence: 99%

Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Helm,

de Silva,

Vogelstein

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The descriptions of the cognitive load and stress datasets are altered versions of the descriptions found in Chen et al (Chen et al, 2022). Unless otherwise stated, the balanced accuracy and the convex coefficient corresponding to each method are calculated using 100 different train-test splits for each participant.…”

Section: Applications To Physiological Prediction Problemsmentioning

confidence: 99%

“…(Kandel et al, 2021)) as features (Akrami et al, 2006;Akbulut et al, 2019;Guerrero et al, 2021). Recent systems for mental state classification have explored more complicated feature sets (Chen et al, 2022) to the tune of some success. Similarly, popular ECG-based predictive systems use simple statistics related to the inter-peak intervals as features for a linear classifier to predict cognitive state (McDuff et al, 2014;Lee et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Helm¹,

Yang²,

Silva³

et al. 2023

Preprint

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We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothesis trained on a new target task. For a particular generative setting we derive the optimal convex combination of the two models under 0-1 loss, propose a computable approximation, and study the effect of various parameter settings on the relative risks between the optimal hypothesis, hypothesis i), and hypothesis ii). We demonstrate the effectiveness of the proposed optimal classifier in the context of EEG-and ECG-based classification settings and argue that the optimal classifier can be computed without access to direct information from any of the individual source tasks. We conclude by discussing further applications, limitations, and possible future directions.

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“…In the early research, Muhammad and Ahmadi [1] developed a framework for the classification of two and four level anxiety using the EEG signals. The authors employed five different classification algorithms out of which random forest (RF) achieved highest classification accuracy of 94.90% and 92.74%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The studies are analyzed based on the type of task, features and classifiers. TableIIIpresents the results in which the methods and the performance measure in terms of accuracy for the comparison of the proposed work in the BCI field include[1,2,[5][6][7][8].…”

mentioning

confidence: 99%

Efficacy of Dimensionality Reduction Techniques and 2D Features in Classification of EEG Data

Chaudhary

Tyagi

2023

Advances in Transdisciplinary Engineering

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EEG-based BCIs enable a direct path for the human brain to communicate with external devices. The main objective of BCI is to classify brain signals to control some applications of interest. EEG is a paradigm that helps to obtain the brain signals from the different states of the human brain. EEG signal feature extraction is a critical topic in brain signal processing that has attracted great attention nowadays with the emergence of large-scale data. The focus of this paper is to reduce the dimension and redundancy of raw EEG data to facilitate retrieval of selected dynamic information. Three approaches of feature extraction and dimensionality reduction viz. PCA, LDA and ICA have been implemented on EEG datasets recorded during attention and meditation state of the brain. Moreover, as massive amount of information about the relationship between time and frequency coefficients is mostly missing in EEG dataset. hence, 1D feature set has been transformed into 2D images. The efficacy of the proposed methods has been tested through the CNN classifier for the classification of EEG signals. The experimental results show that the proposed method is able to achieve an average classification accuracy of 100%.

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Mental State Classification Using Multi-Graph Features

Cited by 4 publications

References 37 publications

Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Approximately Optimal Domain Adaptation with Fisher’s Linear Discriminant

Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis

Efficacy of Dimensionality Reduction Techniques and 2D Features in Classification of EEG Data

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