Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

Fourati, Rahma; Ammar, Boudour; Medina, Javier; Alimi, Adel M.

doi:10.1109/taffc.2020.2982143

Cited by 43 publications

(10 citation statements)

References 59 publications

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“…Over the last years, deep learning networks were used for EEG classification tasks, including seizure detection [19], emotion recognition [20] [21] [22], and classification of motor (imagery) tasks. Various studies showed that LSTMs outperform other models like decision trees, support vector machines used in our previous work [23], logistic regressions, random forest classifiers, naïve Bayes, feedforward neural networks, deep belief networks, and even CNNs for some tasks [24].…”

Section: A Lstm Modelmentioning

confidence: 99%

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

Baghdadi¹,

Fourati²,

Aribi³

et al. 2023

Preprint

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<div>Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.</div>

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Section: A Lstm Modelmentioning

confidence: 99%

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

Baghdadi¹,

Fourati²,

Aribi³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…#5, we consider two multi-class emotion recognition tasks based on the valence, arousal, and dominance levels. Each emotion coordinate's high/low level in the valencearousal-dominance model could be mapped into the Plutchik Wheel emotion model [31] as mentioned above. Here we consider such two tasks from the involved dataset: the 4class classification in DEAP and the 8-class classification in DREAMER.…”

Section: G Exp #5: Emotion Recognition With Multi-class Assessmentsmentioning

confidence: 99%

“…Recurrent models like reservoir computing [31], attentionbased convolutional recurrent neural network [32] was also involved in the EEG-based affection computing.…”

Section: Introductionmentioning

confidence: 99%

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Yan,

Wu,

et al. 2022

Preprint

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Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

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“…Some conventional methods extract important features from signals by using ESNs for the purpose of anomaly detection [17]- [21]. A part of them extract and classify features simultaneously [22], [23]. However, most of such systems need an additional classifier, which spoils the advantage of the short training time in the ESNs [24].…”

Section: Introductionmentioning

confidence: 99%

Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

Kato¹,

Tanaka²,

Nakane³

et al. 2022

Preprint

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We propose reconstructive reservoir computing (RRC) for anomaly detection working for time-series signals. This paper investigates its fundamental properties with experiments employing echo state networks (ESNs). The RRC model is a reconstructor to replicate a normal input time-series signal with no delay or a certain delay (delay ≥ 0). In its anomaly detection process, we evaluate instantaneous reconstruction error defined as the difference between input and output signals at each time. Experiments with a sound dataset from industrial machines demonstrate that the error is low for normal signals while it becomes higher for abnormal ones, showing successful anomaly detection. It is notable that the RRC models’ behavior is very different from that of conventional anomaly detection models, that is, those based on forecasting (delay < 0). The error of the proposed reconstructor is explicitly lower than that of a forecaster, resulting in superior distinction between normal and abnormal states. We show that the RRC model is effective over a large range of reservoir parameters. We also illustrate the distribution of the output weights optimized through a training to discuss their roles in the reconstruction. Then, we investigate the influence of the neuronal leaking rate and the delay time shift amount on the transient response and the reconstruction error, showing high effectiveness of the reconstructor in anomaly detection. The proposed RRC will play a significant role for anomaly detection in the present and future sensor network society

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Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

Cited by 43 publications

References 59 publications

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

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