EEG-Based Emotion Classification Using Graph Signal Processing

Saboksayr, Seyed Saman; Mateos, Gonzalo; Çetin, Müjdat

doi:10.1109/icassp39728.2021.9414342

Cited by 16 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of studies have also shown promising results in applying GSP techniques in classification, dimensionality reduction, and denoising of EEG signals [32][33][34][35][36][37]. In [32], network harmonics of the brain structural connectivity graph are derived for tracking fast spatiotemporal cortical dynamics.…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

View full text Add to dashboard Cite

Objective: This paper presents a graph signal processing (GSP)-based approach for decoding two-class motor imagery EEG data via deriving task-specific discriminative features. Methods: First, a graph learning (GL) method is used to learn subject-specific graphs from EEG signals. Second, by diagonalizing the normalized Laplacian matrix of each subject graph, an orthonormal basis is obtained using which the graph Fourier transform (GFT) of the EEG signals is computed. Third, the GFT coefficients are mapped into a discriminative subspace for differentiating two class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT). Finally, an SVM classifier is trained and tested on the variance of the resulting features to differentiate motor imagery classes. Results: The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to i) using features extracted on a graph constructed by Pearson correlation coefficients and ii) three state-of-the-art alternative methods. Conclusion: Experimental results indicate the superiority of the proposed method over alternative methods, reflecting the added benefit of integrating elements from GL, GSP and FKT. Significance: The proposed method and results underpin the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate motor imagery classes.

show abstract

Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Concluding remarks are given in Section 6, while some technical details are deferred to the appendices. This journal paper offers a more thorough treatment of online discriminative graph learning relative to its short conference precursors [1,2]. This is achieved by means of expanded technical details, discussions and insights, as well as through more comprehensive performance evaluation studies with synthetic and real data experiments.…”

Section: Proposed Approach and Contributionsmentioning

confidence: 99%

“…This is achieved by means of expanded technical details, discussions and insights, as well as through more comprehensive performance evaluation studies with synthetic and real data experiments. In particular, focus in [1] is on emotion classification from EEG signals (the subject of Section 5.3), while [2] deals with online topology identification without a classification task in mind; see also Remark 2. Notational conventions.…”

Section: Proposed Approach and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Online Discriminative Graph Learning from Multi-Class Smooth Signals

Saboksayr¹,

Mateos²,

Çetin³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and inference tasks such as supervised classification is heavily dependent on the prior identification of the relational structures. Graphs are natural descriptors of the relationships between entities of complex environments. The underlying graph is not readily detectable in many cases and one has to infer the topology from the observed signals which admit certain regularity over the sought representation. Assuming that signals are smooth over the latent class-specific graph is the notion we build upon. Firstly, we address the problem of graph signal classification by proposing a novel framework for discriminative graph learning. To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph while maintaining non-smoothness with respect to the graphs corresponding to other classes. Discriminative features are extracted via graph Fourier transform (GFT) of the learned representations and used in downstream learning tasks. Secondly, we extend our work to tackle increasingly dynamic environments and real-time topology inference. To this end, we adopt recent advances of GSP and time-varying convex optimization. We develop a proximal gradient (PG) method which can be adapted to situations where the data are acquired on-the-fly. Beyond discrimination, this is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly. The introduced online framework is guaranteed to track the optimal time-varying batch solution under mild technical conditions. The validation of the proposed frameworks is comprehensively investigated using both synthetic and real data. The proposed classification pipeline

show abstract

“…Prior information of MTMC EEG signal classification [9] can also be utilised using GSP by embedding smoothness of signal variation between nodes and bandlimitedness in the graph spectral domain etc. [23], [24] Motivated by this, learning the mental task based functional brain connectivity from multi channel EEG signal using GSP technique is addressed in this paper. We propose graph signal representation of EEG signals corresponding to each task, where each channel corresponds to the nodes of the graph representing the different regions of the brain and the observations over all channels correspond to the graph signal.…”

Section: Introductionmentioning

confidence: 99%

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Mathur

Chakka

2022

IEEE Sensors J.

View full text Add to dashboard Cite

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multichannel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

show abstract

EEG-Based Emotion Classification Using Graph Signal Processing

Cited by 16 publications

References 25 publications

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Online Discriminative Graph Learning from Multi-Class Smooth Signals

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Contact Info

Product

Resources

About